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Diurnal Metrics for Evaluating GFDL and Other Climate...
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A proposal submitted to NOAA for
NOAA Climate Program Office FY 2015 – NOAA-OAR-CPO-2015-2004099
MAPP Competition: Process-oriented evaluation of climate and Earth system models and
derived projections (Area A, Type 2): Competition ID: 2488569
CFDA Number: 11.431: Climate and Atmospheric Research
Diurnal Metrics for Evaluating GFDL and Other Climate Models
October 16, 2014
Dr. Aiguo Dai, Lead Principal Investigator
Associate Professor
University at Albany – SUNY Dept. of Atmospheric & Environmental Sciences
1400 Washington Avenue, Albany, NY 12222
Tel. 518-442-4474; email: [email protected]
Dr. Jean-Christophe (Chris) Golaz, Co-I
Physical Scientist
NOAA Geophysical Fluid Dynamics
Laboratory (GFDL)
201 Forrestal Road, Princeton, NJ 08540
Tel: 609-452-6523; email:
Dr. Junhong Wang, Co-PI
Research Associate Professor
University at Albany – SUNY Dept. of Atmospheric & Environmental Sciences
1400 Washington Avenue, Albany, NY 12222
Tel. 518-442-3478; email:
Dr. Ming Zhao, Co-I
Project Specialist III
NOAA Geophysical Fluid Dynamics
Laboratory (GFDL)
201 Forrestal Road, Princeton, NJ 08540
Tel. 609-452-6500; email:
Institutional Representatives:
Jessie L. Beauharnois
Senior Administrative Staff Associate
The Research Foundation for The State
University of New York, University at Albany
Office for Sponsored Programs, MSC 312,
1400 Washington Avenue, Albany, NY 12222
Tel. 518-437-8663 Fax: 518-437-8758
Email: [email protected]
Wendy Marshall
Budget Analyst
NOAA Geophysical Fluid Dynamics
Laboratory
201 Forrestal Road
Princeton, New Jersey 08542
Tel. 609-452-6587
Email: [email protected]
DUNS Number: 152652822
Duration of the Project: 1 August 2015 – 31 July 2018
Funding requested:
Year 1 Year 2 Year 3 Total 3 Years
SUNY: Dr. Aiguo Dai $143,198 $143,192 $143,198 $429,588
GFDL: Dr. Jean-Christophe Golaz $6,776 $6,776 $6,776 $20,328
TOTAL: $149,974 $149,968 $149,974 $449,916
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MAPP Competition: Process-oriented evaluation of climate and Earth system models and derived
projections (Area A, Type 2)
1. Abstract
The diurnal cycle is a fundamental feature of Earth's climate. Because of its short time scales and
close coupling to surface and atmospheric processes, the simulation of the diurnal cycle provides an ideal
test bed for evaluating many aspects of model physics. Despite recent improvements in model resolution
and parameterizations, the diurnal amplitude and phase in surface temperature, cloudiness, convection,
precipitation and other fields still differ considerably from observations in many climate models. These
diurnal biases reflect deficiencies in various physical processes simulated by the models. While there
exist many observational datasets with sub-daily resolution, most of them cannot be readily used to
evaluate models, and current model evaluation packages often contain very limited data for evaluating the
diurnal cycle. Based on our previous work on studying the diurnal cycle and its simulation in models,
here we propose to a) develop a new set of diurnal metrics and link them to specific underlying
processes for evaluating model physics, and b) apply the diurnal metrics to diagnose and identify
deficiencies in the GFDL and other CMIP5 models.
Specifically, we propose to 1) compile a new dataset with high temporal-resolution (hourly to 6-
hourly) from surface and satellite observations, field experiments, research sites, and atmospheric
reanalyses for studying the diurnal cycle and evaluating models; 2) apply the new dataset to quantify the
diurnal cycle and study its underlying processes in various fields over the globe, including surface daily
maximum (Tmax) and minimum (Tmin) temperatures, precipitation frequency, intensity and amount,
cloud cover, humidity and others; 3) design a new set of effective diurnal metrics and link them to
specific physical processes based on analyses of observational data; and 4) apply these diurnal metrics
and associated linkages to physical processes to diagnose deficiencies in GFDL and other CMIP5 models
by analyzing sub-daily output from these models.
The new diurnal data set and diurnal metrics developed in this project will greatly enhance current
model evaluation packages. Our second task will improve our understanding of the diurnal cycle and its
underlying physical processes. This understanding is necessary for developing constructive diurnal
metrics for evaluating physical processes in models, while tasks 3 and 4 will directly help improve
models, especially the GFDL model.
A unique feature of this proposal is that it utilizes the expertise of the PI and others on this proposal
in studying the diurnal cycle to identify specific physical processes underlying each of the major diurnal
variations (e.g., in Tmax and Tmin or the low-level jet over the central U.S.), so that a modeler can use
this information to examine specific areas in his/her model when a diurnal bias is found. Another strength
is that it includes two leading modelers from GFDL who have a strong desire to improve the simulation
of the diurnal cycle in GFDL's new models. This collaboration will lead to real model improvements.
Relevance: This proposal is for MAPP Competition - Process-oriented evaluation of climate and Earth system
models and derived projections (Area A, Type 2), which emphasizes projects to "develop and apply
process-oriented metrics to evaluate simulated climate phenomena with strong theoretical and
observational bases". The diurnal cycle is a well-studied, fundamental feature of Earth's climate. The
focus of our diurnal metrics on the sub-daily processes and our emphasis on linking diurnal biases to
underlying physical processes make our metrics truly process-oriented. We will also apply the new
diurnal metrics to diagnose the simulation of the diurnal cycle in the GDFL and other models. Thus, this
proposal is directly responsive to the MAPP competition. Improving climate models and our
understanding of the diurnal cycle is also an important step to achieve NOAA's long-term climate goal to
"improved scientific understanding of the changing climate system and its impacts".
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2. Results from Prior Research The PI (A. Dai) has previously analyzed various surface and satellite data to study the diurnal cycle
in surface air temperature, pressure, humidity, winds, and precipitation, as well as the diurnal cycle in
climate models (see refs. cited in section 3.1). However, Dr. Dai has not had any specific projects in the
last 3 years funded by NOAA or other agencies that are relevant to this proposal, besides the one
mentioned below on homogenization of radiosonde data. During the last three years, Dr. Dai has
continued his diurnal research through collaborations with Curt Covey on evaluation of the pressure tides
in CMIP3 and CMIP5 models (Covey et al. 2011; 2014). He also updated his diurnal analysis of CMIP3-
model simulated precipitation (Dai 2006a) to include CMIP5 models, and provided an updated figure (cf.
Fig. 1) to IPCC Fifth Assessment Report (AR5, its Fig. 9.30). The PI's expertise on the diurnal cycle and
model diagnostics is critical for the proposed work, especially for designing the diurnal metrics and
linking them to specific underlying physical processes.
Co-PI J. Wang has been and continues to be involved in the following projects on creating high-
quality, high resolution data to study the diurnal cycle in water vapor and other fields along with other
time scales, and applying them to validate reanalysis products. Since 2011, Dr. Wang has been supported
by NASA ROSES program to update and improve her global, 2-hourly ground-based GPS precipitable
water (PW) dataset (Wang et al. 2007). The dataset has been used to study the PW diurnal variability,
validate radiosonde and satellite data and reanalysis products, and document the diurnal asymmetry in
PW trends and its correlation with temperature (Wang and Zhang 2008; Wang and Zhang 2009; Wang et
al. 2013; Wang et al. 2014). During 2010-2013, Dr. Wang and Dr. Dai led a project supported by NOAA
Climate Program Office on homogenizing global radiosonde humidity data and characterizing upper air
humidity variability. This project produced several journal publications (e.g., Dai et al 2011; Zhao et al.
2012; Wang et al. 2013a; Wang et al. 2014) and a homogenized radiosonde data set that has been used by
several groups already. From 1999 to August 2014, Dr. Wang worked on quality-control, bias-correction
and analysis of sounding data from various field projects (e.g., Wang et al. 2002, 2003; Ciesielski et al.
2014). The sounding data have been used to validate satellite and reanalysis products, and to create
gridded, 3-hourly or 6-hourly upper air datasets for projects with special designed sounding arrays (e.g.,
Wang et al. 2010; Wang et al. 2013b; Johnson et al. 2014).
Dr. Golaz has extensive experience with the development and evaluation of global climate models.
He contributed to the development of the CMIP5 generation GFDL AM3 model and is now co-leading
the team tasked with the development of the next generation atmospheric component AM4. Dr. Golaz
authored several publications related to the treatment of clouds in AM3 and the impact of the aerosol
indirect effects (Golaz et al. 2011, Golaz et al. 2013, Suzuki et al. 2013). PBL properties of AM3 were
also analyzed in the context of an intercomparison with other global models (Zhang et al. 2011, Seidel et
al. 2012). As part of a CPT project, Dr. Golaz also helped implement a new unified cloud and turbulence
parameterization in AM3 (CLUBB, Guo et al. 2014). This new parameterization unifies the treatment of
PBL mixing, shallow convection and cloud macro-physics scheme.
Dr. Zhao is currently a co-lead of atmospheric model working group (AWG) and coupled model
working group (CWG) for development of GFDL’s next generation climate model (AM4/CM4).
Previously, Zhao is a core developer of GFDL global high-resolution atmospheric model (HiRAM, Zhao
et al. 2009) and the atmospheric model version 3 (AM3, Donner et al. 2011). Zhao’s most recent work has
been focusing on developing a new double-plume convection scheme (DPC) for AM4/CM4. A new
version of HIRAM with the DPC scheme has been tested in both multi-decadal climate simulations forced
by the observed SSTs or radiative gases concentrations and various short-range (a few days to a season)
forecast experiments with initial values set to observed conditions. We have found many improvements in
climate simulations including both mean climate and tropical variability such as MJO statistics. The
causes of the model’s improvements are currently under exploration. Dr. Zhao is the lead PI on grant
recently funded by NOAA CPO (started in Sept 2014) to perform "Process Level Investigation of the Role
of Convection and Cloud Parameterization in Tropical Pacific Bias in GFDL Next Generation Global
Climate Models". He is also Co-PI on a NOAA CPO/MAPP grant led y X. Jiang of UCLA to study
MJO's influence on tropical cyclones (started in Sept 2012).
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3. Statement of Work 3.1 Introduction and the Problem Forced by the solar diurnal cycle, Earth's weather and climate show large sub-daily variations at
24-hr and shorter time scales (referred to as diurnal variations or the diurnal cycle hereafter) in air
temperature (Dai et al. 1999a), pressure (Dai and Wang 1999), winds (Dai and Deser 1999; Dai and
Trenberth 2004), cloudiness (Dai and Trenberth 2004), convection and precipitation (Dai 2001; Dai et al.
2007), water vapor (Dai et al. 2002) and other fields (Lin et al. 2000; Seidel et al. 2012). Because of its
short time scales and close coupling to near-surface and atmospheric processes, simulations of the diurnal
cycle in weather and climate models provide an excellent test bed for evaluating various model physics,
such as surface sensible heat (SH), latent heat (LH) and radiative fluxes, the planetary boundary layer
(PBL), cloud formation, moist convection, and precipitation process.
Many studies have examined model-simulated diurnal variations in surface temperature, pressure,
precipitation, the PBL height, and other fields (Randall et al. 1991; Holtslag and Boville 1993; Dai et al.
1999b; Lin et al. 2000; Dai & Trenberth 2004; Dai 2006a; Ploshay & Lau, 2010; Wang et al. 2011; Seidel
et al. 2012; Covey et al. 2011, 2014; Lindvall et al. 2013; Lindvall & Svensson, 2014). These studies
show that while most climate models are able to reproduce the observed diurnal and semidiurnal
variations in surface pressure fields (i.e., the pressure tides), many of them still have difficulties in
simulating the observed diurnal cycle in surface air temperature, moist convection, cloud cover,
precipitation (Fig. 1), PBL height, and other related surface fields, although improved resolution (Sato et
al. 2009; Ploshay and Lau 2010; Wang et al. 2011) and parameterizations (Khairoutdinov et al. 2005;
Stratton and Stirling 2012; Hourdin et al. 2013) help models simulate the diurnal cycle. In particular,
many climate models tend to rain too frequently at reduced intensity (i.e., drizzling with a weak diurnal
cycle) (Dai 2006a; Stephens et al. 2010), leading to their limited abilities in simulating heavy
precipitation and other extreme events. This is a major deficiency that limits a model's ability to simulate
future changes in extreme events under increased greenhouse gases (GHGs). Another issue is that many
coupled models lack a diurnal cycle in sea surface temperatures (SSTs) due to the coarse vertical
resolution in upper oceans and daily coupling between the atmosphere and oceans. This leads to a weak
diurnal cycle in air temperature and other related fields over the oceans (Dai and Trenberth, 2004) and
causes other problems (Danabasoglu et al., 2006; Bernie et al., 2008).
GFDL climate models (Delworth et al. 2006; Zhao et al. 2009; Donner et al. 2011; Guo et al. 2014)
are among the most respected and widely used models in climate research and projection. However, like
all other models used in the CMIP5 project (Taylor et al. 2012), the GFDL models still suffer from
deficiencies and would benefit from improvements in various aspects. In particular, the simulations of the
diurnal cycle by GFDL models still contain large biases, including a too small diurnal temperature range
(DTR) over the U.S. and other land areas, a near-noon peak (instead of a late-afternoon peak as in
observations) in convective and total precipitation over land areas (Fig.2).
The diurnal biases in the GFDL and other models reflect deficiencies in various model-simulated
physical processes, including the diurnal evolution of the near-surface heating, vertical turbulence mixing
of heat, water and momentum, the PBL, shallow and deep convective clouds, moist convection, etc. By
analyzing the model processes underlying the simulated diurnal variations, it is possible to diagnose and
identify the problems in model physics and parameterizations that lead to the diurnal biases. However,
this type of analysis requires a deep understanding of the underlying processes behind each diurnal cycle,
and the use of high-resolution data, for which the CMIP5 archive of model data is insufficient.
Furthermore, an in-depth understanding of the model physics is also necessary for such a diagnostic
analysis. Thus, a close collaboration with the modelers is essential. However, it is almost impossible to
have close interactions with many of the CMIP5 modeling groups at the same time. Partly because of
these reasons, most previous analyses of model-simulated diurnal cycle have focused on the comparison
of the mean diurnal variations between observations and models, but often fallen short of linking the
diurnal biases to potential problems in specific model physics or parameterizations. Because of this, these
studies are usually not directly beneficial to modelers for diagnosing and improving their models.
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Fig. 1. Composite diurnal cycle of precipitation for land and ocean latitude zones derived from 3-hourly data from
CMIP5 models. For most of the models, data from 1980-2005 from "historical runs" were averaged to derived the
composite cycle. The best models include MIRCO-ESM, CMCC-CM, and inmcm4. Different versions of the
models from the same center showed similar diurnal cycle for precipitation. Many models still tend to have a peak
precipitation soon after noon, several hours too early compared to surface (OBS) and TRMM satellite observations,
although the best performing models appear to be able to capture the land and ocean diurnal phase and amplitude
quite well. This is an update to Fig. 17 of Dai (2006a) and used by IPCC AR5 as its Fig. 9.30.
FIG.2: (a) Long-term (1981-2000) mean June-August (JJA) differences (oC) between model-simulated (GFDL
prototype AM4 model) and observed (from PRISM) surface diurnal temperature range (DTR). (b) JJA composite
diurnal cycle (departures from the daily mean in mm/day) averaged over global (50oS-50
oN) land for prototype
AM4 total (blue), convective (pink), and large-scale (brown) precipitation, compared with TRMM observed total
precipitation (green)
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Here we propose to develop a new set of diurnal metrics and establish the linkages between
these metrics and their controlling physical processes as seen in observations and reanalyses, so that
modelers can use these metrics to evaluate and diagnose specific underlying processes simulated in
their models. Without the linkage to specific physical processes, a diurnal bias may not be very
constructive for improving model physics as many modelers may not have the necessary expertise to link
such a bias to its underlying processes. We recognize that the diurnal cycle in one particular field (e.g.,
deep convection) is often coupled to several factors or processes, and it may be impossible to link a
diurnal bias (e.g., in convective precipitation) to one or two specific factors or processes. Nevertheless,
the physical processes underlying diurnal variations in surface pressure, daily maximum (Tmax) and
minimum (Tmin) temperature, DTR, SH, LH, and others are understood well enough so that a likely
linkage between a diurnal bias in each of these variables and specific physical processes can be
established. Such a linkage is critical for modelers to use the diurnal metrics to diagnose model
deficiencies.
We have formed a team of experts from SUNY Albany and GFDL to carry out the proposed work.
The lead PI (A. Dai) has done and published considerable amount of research on the diurnal cycle and its
depiction in NCAR and other climate models. He has an in-depth understanding of the characteristics and
the underlying physical processes for the diurnal cycle in many climate fields. Other members include Dr.
Junhong Wang, who is an expert on water vapor, atmospheric sounding, and field campaign datasets, and
Drs. Chris Golaz and Ming Zhao, who are experts in the study and modeling of the PBL, clouds and
convection. Chris and Ming jointly lead the GFDL Model Development Team Atmospheric Working
Group (AWG) for the development of GFDL's next atmospheric model, and they have a strong desire to
improve the diurnal simulations in GFDL's new models through this collaborative research. Thus, this
proposal combines the strength of the PI and co-PI from SUNY Albany in diurnal studies, datasets
and diagnostic analysis with the expertise in model development from GFDL's leading modelers.
This unique combination of expertise has the potential to make real model improvements through
the diurnal analysis work proposed below.
3.2 Scientific Objectives
The main objective of this proposal is to develop a new set of constructive diurnal metrics that
can guide modelers to identify problems in simulating certain physical processes in their models. A
secondary goal is to improve our understanding of the physical processes underlying major diurnal
variations. Such an improved understanding is necessary for designing these diurnal metrics and for
establishing their linkages to specific physical processes.
Although the diurnal cycle is evident in many climate fields, designing useful diurnal metrics that
can be compared with available observations and help reveal problems in underlying model physics
requires in-depth understanding of the physical processes behind the diurnal variations in the real world.
It also requires a good knowledge of the available observations with high temporal-resolution from in-situ
measurements and remote sensing. For example, the DTR in surface air temperature has been measured at
thousands of weather stations around the world, and it is closely coupled with cloud cover, surface
evaporation, surface albedo, and other near-surface processes (Dai et al. 1999a). Thus, biases in model-
simulated DTR can be compared with observations and these biases can be used to diagnose problems in
simulating these related fields. Another example is that warm-seasonal low-level clouds and moist
convection over land and coastal regions exhibit a large diurnal cycle, typically with shallow cumulus
clouds developing before noon, deep cumulus clouds appear in early afternoon, and thunderstorms occur
in late afternoon over the continents, as shown in surface and satellite observations (Dai et al. 2007),
although exceptions to this general pattern do exist over some regions such as the central Great Plains in
the U.S. (Dai et al. 1999b; Liang et al. 2004). This diurnal evolution of clouds and convection can be used
to diagnose problems in model parameterizations for the PBL, clouds, shallow and deep convection, as
well as surface energy fluxes.
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Thus, the design of the diurnal metrics and interpretation of model biases in these metrics involve
considerable knowledge about the physical processes behind the diurnal variations measured by these
metrics. A simple comparison of a diurnal metric (e.g., DTR) between observations and models without
proper understanding of the physics behind it will not be very useful to many modelers. A unique feature
of this proposal is that we will use our expertise from studying the diurnal cycle, the PBL, clouds
and convection to design appropriate diurnal metrics and link them to specific physical processes in
models, thereby helping modelers find specific areas in their models that may require
improvements given the biases revealed by the diurnal metrics. Thus, our diurnal metrics and the
linkages to specific physical processes will be helpful to not only our collaborators at GFDL, but also
other modeling groups, including those participated in CMIP5.
3.3 Proposed Work and Methodology
Specifically, we propose to 1) gather and compile sub-daily (hourly to 6-hourly) data from
observations and other sources (Table 1), 2) analyze these data to improve our understanding of the
physical processes behind the diurnal variations in various fields, 3) develop a new set of diurnal metrics
and establish the link to their underlying physical processes, and 4) apply the new diurnal metrics to
evaluate and diagnose model physics in the GFDL and other CMIP5 models. These individual tasks are
described below.
3.3.1 Compilation of sub-daily data from observations and reanalyses
We will first collect, quality-control, compile and synthesize available high temporal-resolution
data from observations and reanalysis products (Table 1) to produce a high-quality, comprehensive
dataset for diurnal analyses in this project and by others, including climate modeling groups.
There are numerous hourly, 3-hourly and 6-hourly data from weather and radiosonde stations (Dai
2001; Dai et al. 2011), special research projects (such as the ARM sites, Xie et al. 2010) around the globe,
special sounding arrays from field campaigns such as the IHOP (Weckwerth et al. 2004) over the Great
Plains, the TOGA-COARE (Webster and Lukas 1992) in the Western Pacific and the DYNAMO
(Yoneyama et al. 2013) over the Indian Ocean, as well as satellite observations (e.g., TRMM 3-hourly
products, Huffman et al. 2007), and reanalysis products (e.g., 3-hourly data from NARR, CFSR and
MERRA). Table 1 summarizes the major datasets that can be used in our analysis.
The sounding networks from various field projects (Fig. 3) show extensive spatial coverage. Until
August 2014, Co-PI J. Wang had been the lead scientist at NCAR in charge of the data quality control of
these sounding data from various field campaigns. We have extensive experiences in using the surface
and satellite data to study the diurnal cycle (e.g., Dai and Wang 1999; Dai and Deser 1999; Dai 2001; Dai
2006b; Dai et al. 2007; Wang and Zhang 2008; Wang et al. 2014) and in analyzing high-resolution model
output (e.g., Dai and Trenberth 2004; Dai 2006b). We have also used high-resolution data from field
experiments to study the diurnal cycle (e.g., Dai et al. 1999a) in surface temperature and the warm-season
precipitation over the central U.S. For example, Fig. 4 shows that related to the differential heating
between the Rockies and the Great Plains there exists a diurnal circulation over these regions that
suppresses daytime convection and favors nighttime convection over the Great Plains. Climate models
may need finer resolution (<100km) to simulate this regional diurnal circulation and thus the nocturnal
precipitation maximum in the central U.S.
Many of the individual datasets listed in Table 1 are not designed for comparisons with climate
models and they cannot be readily used in model evaluations (except for the gridded ones). As a result,
many model evaluation packages include only very limited data with sub-daily resolution. For example,
59 out of the 64 datasets included in the Observations for Climate Model Intercomparisons (Obs4MIPs)
project (https://www.earthsystemcog.org/projects/obs4mips/) contain monthly mean data, and only one
has sub-daily temporal resolution (3-hourly TRMM precipitation). We will examine and analyze the
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various data sets listed in Table 1 and compile a comprehensive diurnal data set that can be readily used
for model evaluations. In particular, we will use the compiled data set to compute our diurnal metrics and
to study the physical processes underlying these metrics.
FIG. 3: Distribution of field projects with high-resolution hourly sounding observations that can be used to evaluate
climate models. (From Johnson et al. 2012)
FIG. 4: Longitudinal variations of zonal and vertical wind profiles (arrows, from NARR) averaged from
N to 42N and over days without precipitation during the IHOP period from 13 May to 25 June 2002 at
(a) 09 UTC and (b) 21 UTC. The colors show vertical pressure velocity (in Pa/s, positive downward,
from NARR) in (a) and (b). (c) Diurnal cycle of vertical wind velocity profiles over the U.S. central Great
Plains averaged over days without precipitation during the IHOP period from NARR (in Pa/s).
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Table 1. Observational and reanalysis data sets to be used in this project. Dataset Name
(Reference)
Spatial &Temporal
Resol. & Coverage
Data Sources and Merging Method Online Documentation
Surface met data
3-hrly surface
synoptic data (Dai
2006b)
4°(lat)5°(lon),
globe; 3-hrly,
1975-2004
Surface synoptic obs from >15,000
stations, and ships; temperature,
humidity, pressure, clouds, precipitation
occurrence, and winds
Land:http://rda.ucar.edu/datase
ts/ds464.0/
Ocean: http://icoads.noaa.gov/
GHCN-daily
(Menne et al. 2012)
>90,000 stations,
globe land; daily,
1880-present
Land-based surface station data;
including Tmax, Tmin, precipitation,
snow and others
http://www.ncdc.noaa.gov/oa/c
limate/ghcn-daily/
Precipitation data
CMORPH1
(Joyce et al. 2004)
0.25o grid, 60°S -
60°N, 180°W -
180°E; 30 min.,
12/2002-present
Satellite microwave estimates and the
TRMM (TMI7) satellites are propagated by
motion vectors derived from geostationary
satellite infrared data.
http://www.cpc.ncep.noaa.gov/
products/janowiak/cmorph_des
cription.html
PERSIANN2
(Hsu et al. 1997)
0.25o grid, 60°S -
60°N, 180°W -
180°E; 30 min.,
3/2000-present
A neural network, trained by precipitation
from TRMM TMI (2A12) and other
satellites was used to estimate 30 min.
precipitation from infrared images from
global geo-satellites.
http://chrs.web.uci.edu/persian
n/data.html
TRMM 3B42
(Huffman et al.
2007)
0.25o grid, 50°S -
50°N, 180°W -
180°E; 3-hourly,
1/1998-present
Microwave precipitation estimates were
used to adjust IR estimates from
geostationary IR observations. The rainfall
estimates were scaled to match the monthly
rain gauge analysis used in TRMM 3B-43.
http://daac.gsfc.nasa.gov/preci
pitation/TRMM_README/T
RMM_3B42_readme.shtml
CPC Hourly US
Precipitation
(Higgins et al. 1996)
2.5 o lon 2.0o lat,
20oN-60oN, 140oW-
60oW; hourly,
7/1948-10/2002
Hourly reports from ~2800 rain gauges
were used to derive the gridded data.
http://www.cpc.ncep.noaa.gov/res
earch_papers/ncep_cpc_atlas/1/toc
.html
http://www.esrl.noaa.gov/psd/data/
gridded/data.cpc_hour.html
Global precipitation
frequency (Dai
2001)
2o grid, global; 3-
hourly for each
season, 1976-1997
Weather reports from ships and >15,000
stations were used to compile the
occurrence frequency for various types
of precipitation
http://www.cgd.ucar.edu/cas/a
dai/data-dai.html
GPCP v2.2
(Huffman et al.
2009)
2.5o grid, globe,
monthly, 1979-
present
IR estimates were calibrated by
microwave estimates and then adjusted
by rain-gauge data
http://precip.gsfc.nasa.gov/ http://www.esrl.noaa.gov/psd/data/
gridded/data.gpcp.html
Radiosonde data
6-hrly radiosonde
dataset (Seidel et al.
2005)
53 stations, globe;
3- or 6-hourly, 4-
mon to 4 years
Radiosonde data from global radiosonde
archive, TOGA_COARE and ARM
projects
Seidel et al. (2005)
IGRA2 6-hrly
radiosonde dataset
13 or more stations
(Fig.1), globe; 6-
hourly, various
NCDC IGRA Version 2 (more data,
longer records, including ships and Ice
islands)
http://www1.ncdc.noaa.gov/pu
b/data/igra/v2beta/
ARM
SONDEWRPR
ARM SGP sites; 3-6
hrly, 1994-present.
ARM Radiosonde data from SGP sites http://www.arm.gov/data/datas
treams/sondewrpr
High resolution
sounding data
(Johnson et al.2012)
Various sites,
globe; 3-6 hrly,
1958-present
High resolution radiosonde data from
various field projects (see Fig. 3)
http://www.eol.ucar.edu/projec
ts/legacy/
GPS data
A global, 2-hrly
GPS PW dataset
(Wang et al. 2007)
>400 stations,
globe; 2-hrly,
1995-present
Precipitable water (PW) derived from
ground-based GPS measurements
http://rda.ucar.edu/datasets/ds7
21.1/
Suominet data
(Ware et al. 2000)
~900 stations, globe;
30-min, 1995-present PW derived from ground-based GPS
measurements; concentrated in USA
http://www.suominet.ucar.edu/
data.html
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Table 1 (continued) GFZ TIGA reprocessed data (Tong Ning, pers.
comm.)
782 stations, globe;
30min, 1994-2012
Consistently processed GPS PW data http://www.gfz-
potsdam.de/en/research/organizational-units/departments/department-
1/gpsgalileo-earth-
observation/projects/tiga-reprocessing/
Cloud data
ISCCP D1 data
(Rossow and
Schiffer 1999)
280km equal-area-
grid, globe; 3hrly
1983-2009
Cloud amount and other properties
derived from satellite observations
https://eosweb.larc.nasa.gov/pr
oject/isccp/isccp_d1_table
ISCCP WS data
(Rossow et al. 2005) 2.5 2.5, globe;
3hrly 1983-2004
Weather state data derived from ISCCP
D1 data
http://isccp.giss.nasa.gov/clima
nal5.html
Field Project Data Objectives & data
ARM ARMBE data
set (Xie et al. 2010)
ARM sites from
Alaska to the
Tropics, resol.: min.
Clouds, radiation and other data http://www.arm.gov/data/vaps/
armbe
TOGA_COARE
(Webster and Lukas
1992)
1o1o, tropical west
Pacific; 6-hrly,
11/1992-2/1993
Studying convection, air-sea and
multiple-scale interactions; CSU
gridded datasets and others
http://tornado.atmos.colostate.edu/toga
data/gridded.html
http://www.eol.ucar.edu/projects/toga_coare/data.html
IHOP (Weckwerth
et al. 2004)
Five stations, US
SGP; 3-hrly, 5/26-
6/14/2012
Warm-season precipitation diurnal cycle
in SGP; sounding data, derived
CAPE/CIN, NARR and other data
http://data.eol.ucar.edu/master
_list/?project=IHOP_2002
NAME (Higgins et
al. 2006) 1
o1
o, N.A.
Monsoon region;
6-hrly, 7/1-
8/15/2004
N.A. Monsoon, its variability and
impact on N.A. warm season
precipitation; CSU gridded datasets and
others
http://tornado.atmos.colostate.edu/
name/products/gridded/index.html
http://data.eol.ucar.edu/master_list
/?project=NAME
DYNAMO
(Yoneyama et al.
2013)
1o1
o, Indian
Ocean; 3-hrly, 10-
12/2011
Understanding the processes key to
MJO initiation; CSU gridded datasets
and others
http://johnson.atmos.colostate.edu/dyn
amo/products/gridded/index.html
http://data.eol.ucar.edu/master_list
/?project=DYNAMO
FLUXNET
(Lindvall et al.
2013)
35 FLUXNET sites,
globe; 30/60 min.,
1992-2009
Turbulent fluxes, surface met. http://data.eol.ucar.edu/codiac/
dss/id=76.205
GCSS-DIME3 Various, globe;
various
Focus on cloud-climate interactions and
GCM evaluation; both satellite and in-situ
campaign data.
http://gcss-dime.giss.nasa.gov/
Reanalysis data
NARR, CFSR,
MERRA, ERA-
Interim
N.A. (NARR), global,
~0.5 t0 1.0o, 1-, 3- &
6-hrly, 1979-present
Surface and upper air fields, constrained
by satellite and radiosonde observations
and model physics.
http://reanalyses.org/atmospher
e/comparison-table
1 CMORPH = Climate Prediction Center (CPC) morphing method
2 PERSIANN = Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks
3 GCSS-DIME: GEWEX Cloud System Study Data Integration for Model Evaluation
3.3.2 Quantifying the diurnal cycle and identifying its underlying physical processes
Building on previous studies of the diurnal cycle done by ourselves and others (some of them cited
in section 3.1), we will further quantify the long-term mean diurnal amplitude and phase in various fields
to provide a comprehensive view of the mean diurnal cycle and its spatial and seasonal variations, and to
improve our understanding of the physical processes underlying the major diurnal variations such those in
surface temperatures, energy fluxes, cloudiness, convection and precipitation.
The quantification of the diurnal amplitude and phase will be straightforward, either using the
actual daily minimum and maximum of the composite mean diurnal curve (cf. Fig. 2b; e.g., for DTR,
precipitation, etc.) or by fitting the composite diurnal curve with the 24-hr and 12-hr harmonics (e.g., for
pressure tides, temperature, etc., see Dai and Wang 1999). Table 2 lists our choices for specific variables.
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We prefer the method of deriving the composite diurnal curve first and then estimate the diurnal
(and semidiurnal) amplitude and the phase from this curve (as done in all of our previous diurnal studies)
over the method of applying a Fourier transfer directly to the original data series (e.g., Covey et al. 2011,
2014). The latter method requires that the data series be continuous without missing data, which is often
not the case for observations, and it needs more computing time than the first method. Furthermore, the
composite averaging over a large number of days during many years for each local hour removes synoptic
and other variations, leaving only the diurnal variations in the composite curve, which itself depicts the
diurnal cycle nicely (cf. Fig. 2b). The composite method also allows one to stratify the data by seasons.
The diurnal amplitude (A=the maximum minus the daily mean) and local solar time when the maximum
occurs (tmax) derived from high temporal-resolution composite diurnal curves will be used as the diurnal
metrics for many of the diurnal variables listed in Table 2. For cases where only relatively low-resolution
(e.g., 3-hourly or even coarser) data are available, we will fit the data curve with diurnal and semidiurnal
harmonics first and estimate A and tmax from the fitted curve.
A more challenging task will be to establish the physical linkage between a diurnal cycle (e.g., in
temperature) to near-surface and atmospheric processes that control it, similar to what we did for the DTR
in Dai et al. (1999a). Based on our current understanding, in Table 2 we list the likely physical processes
underlying the diurnal cycle in each of the major climate variables. The task here will be to use the newly
compiled diurnal dataset to quantify the linkage between a diurnal cycle and the associated physical
processes (some of them are listed in Table 2). Following Dai et al. (1999a), we will employ composite
analysis (e.g., clear vs. cloudy days, wet-surface vs. dry-surface days, windy vs. calm days, humid vs.
low-humidity days, etc.) to compare the diurnal cycle between different conditions, and other method
(e.g., partial correlations) to identify the major controlling factors or processes responsible for the diurnal
cycle in each of the variables listed in Table 2. We will also quantify the influence (e.g., using correlation
or variance explained) from each of these factors or processes, and derive their proper distributions or
characteristics for a correct representation of the diurnal cycle in a given field. For example, cloud amount
is found to be a major controlling factor for daytime maximum temperature (Tmax; Dai et al. 19999a);
thus, correct spatial and seasonal distributions of mean cloud amount in a model are necessary for it to
realistically simulate the Tmax and thus DTR for the right reason. Sometimes, models may still simulate
the correct Tmax even if cloud amount is incorrect because of cancellation of errors in different fields.
However, one would prefer a model to be correct for the right reason (this also applies to precipitation
frequency, intensity and amount, see Dai 2006a). Thus, it is essential to realistically simulate the cloud
amount first in order to correctly simulate Tmax and DTR in a model.
The linkage information, in additional to the diurnal metric itself, is critical for modelers to
evaluate the underlying factors or processes in their models. Thus, we will include it in our standard data
products from this project, using tables similar to Table 2 but with more specific information (e.g., with
rankings for the controlling factors). Required long-term climatology of these factors (e.g., surface albedo,
cloud cover, and daytime evapotranspiration for Tmax case, etc.) derived from observations and
reanalyses will also be included in the data products.
Our diurnal analysis will focus on three areas: 1) thermal heating near the surface and in the PBL, 2)
tropospheric conditions, and 3) clouds, moist convection, and precipitation. Table 2 summarizes the main
variables and likely underlying physical processes or factors for each of these areas based on our current
understanding. Table 2 provides a general guidance to our diurnal analysis, but new variables and
processes will likely be added as the work progresses.
3.3.3 Development of a new set of diurnal metrics linked to specific underlying processes
Based on the above analysis of the diurnal cycle and its underlying processes, we will develop a
new set of diurnal metrics that are linked to specific processes for evaluating and diagnosing model
physics. Table 2 lists some of the candidate variables and associated metrics. They include diurnal
variations for measuring surface daytime heating (e.g., Tmax, DTR) and nighttime cooling (e.g., Tmin),
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tropospheric conditions (e.g., vertical velocity), and diurnal evolution of low clouds, convection and
precipitation. For most of the variables listed in Table 2, there exist high temporal-resolution data from
observations and/or reanalyses (Table 1), so that a comparison between models and observations or
reanalyses is possible. Some of the variables, such as convective available potential energy (CAPE),
convective inhibition (CIN) and low-level convergence, will need to be derived from other observational
fields such as atmospheric temperature and humidity profiles and winds from sounding observations or
reanalyses.
Besides the availability of observational data, another criterion in selecting the diurnal metrics will
be how strong a metric is coupled to certain specific processes or factors. For example, Dai et al. (1999a)
found that Tmax is tightly coupled to cloud amount through clouds' albedo effect on solar radiation, thus
the metric TmaxTmean, i.e., the deviation of the daily maximum temperature from the daily mean, can be
an effective measure of cloudiness, although surface evaporative cooling, surface reflection of sunlight
and surface SH flux can also influence Tmax. Below we use two examples to illustrate how our diurnal
metrics may help modelers diagnose problems in their models.
Suppose a modeler finds a mean bias in TmaxTmean compared with observed values over a region,
e.g., over the central U.S. in the GFDL AM4 model (Fig. 5a), then the first thing (s)he should check is the
model-simulated cloud amount. If the long-term mean cloud amount is too low (high) compared with
observations, then Tmax-Tmean will likely be too large (small), although compensating errors from the
other processes in the model may complicate this relationship seen in observations. Assuming that the
modeler does find biases in the simulated cloud amount, then (s)he will have to examine and test the
cloud scheme used in the model to find out what may cause the cloud biases. Although this single diurnal
metric will not help the modeler locate the exact problems in the cloud scheme, it is still very constructive
by pointing the modeler in the right direction. Furthermore, other diurnal metrics, such as those related to
the diurnal evolution of clouds and convection (Table 2), can also help the modeler find the problems in
the simulation of clouds. If the modeler finds the cloud amount reasonable, then (s)he will need to check
the other surface fields, and use other diurnal metrics and other diagnostics to find the underlying model
deficiencies.
The diurnal cycle over the U.S. Great Plains (Dai et al. 1999b; Liang et al. 2004) presents another
case for examining many underlying processes in models. For example, a recent study (Du and Rotunna
2014) suggests that spatially and diurnally varying surface heating and friction lead to the observed
diurnal variations in the Great Plain low-level jet (GPLLJ) with an early morning maximum. The diurnal
variations in the GPLLJ are further linked to daytime subsidence (nighttime ascent) over the Great Plains
and the opposite west of it due to local vorticity balance (Pu and Dickinson 2014). Thus, we can use the
diurnal amplitude (A) and phase (tmax) of the GPLLJ (i.e., meridional wind over the Great Plains, plus
precipitation diurnal cycle) as the diurnal metrics and apply them to diagnose the model processes
underlying the diurnal cycle over the Great Plains, including model-simulated surface heating and friction
that control the diurnal variations in the GPLLJ and the related vertical motion over the Great Plains. We
can also examine whether model resolution plays a role in this simulation. It may require climate models
to have a grid size finer than 100km to simulate the meridional and zonal variations in surface heating and
friction (and thus the GPLLJ) and the resultant regional diurnal circulation seen in the North American
Regional Reanalysis (NARR) (Fig. 4). For the GFDL AM4 and NCAR CAM5 models, we will have access
to model output with a grid size finer than 50km. Thus, we will be able to examine whether model
resolution is crucial for simulating the diurnal cycle in the central U.S.
The above examples illustrate the importance of the process-based understanding of the diurnal
variations. Without this knowledge, most diurnal metrics will not be very helpful to modelers. In this
project, we will not only provide the diurnal metrics and their observed values, but more importantly we
will also include the constructive information that links each of the diurnal metrics to specific underlying
physical processes, so that modelers would know where to look for deficiencies when a diurnal bias is
found.
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13
FIG. 5: (a) Long-term (1981-2000) mean June-August (JJA) differences (
oC) between model-simulated
(GFDL prototype AM4 model) and observed (from PRISM) surface daily maximum air temperature
(Tmax). (b) Same as (a) but for surface daily minimum air temperature (Tmin).
3.3.4 Applying the diurnal metrics to diagnose problems in GFDL and other CMIP5 models
Our diurnal metrics and their linkages to specific underlying processes will be available to any
modelers. To demonstrate their usefulness, we will apply them to help diagnose problems in the GFDL
and other select CMIP5 models at the process-level. Besides the GFDL AM4 prototype model, we will
include at least the NCAR CESM (or CAM5) in this analysis. Other CMIP5 models may also be examined,
but the availability of high temporal-resolution data from these models may limit our choices. Our GFDL
collaborators will help us in this task by providing high temporal-resolution output from their model runs
and sharing their expertise in modeling the PBL, clouds, convection and precipitation. Due to the coarse
vertical resolution in upper oceans and daily coupling between the atmosphere and oceans, many coupled
models lack a diurnal cycle in sea surface temperatures (SSTs). This leads to a weak diurnal cycle over
the oceans (Dai and Trenberth, 2004; Danabasoglu et al., 2006) and other problems (Bernie et al., 2008).
As shown by Fig. 2 and Fig.5, the GFDL AM4 prototype model still has large biases in its Tmin,
Tmax and DTR over the US (and likely over other regions too). The warm bias in Tmin (Fig. 5b) suggests
that surface downward longwave (LWdn) radiation may be too high, which allows the surface to maintain
a high Tmin at night. The surface LWdn is controlled primarily by air temperature and water vapor
content and secondarily by cloud amount in the lower troposphere (Zhang et al. 1995). Since the surface
and lower tropospheric temperatures are tightly coupled with each other, to maintain a warm bias in these
temperatures the model has to have a strong greenhouse warming effect from the mid-upper troposphere
(otherwise, the lower troposphere will cool to a lower temperature). The most likely way to do that is
through an enhanced warming by excessive water vapor in the middle to upper troposphere, although
excessive mid-high level clouds could theoretically also enhance the warming on the surface and the
lower troposphere, but the Tmax biases (Fig. 5a) do not suggest a systematic bias in cloudiness across the
US. In fact, Fig. 5a suggests too few clouds over the central U.S. Thus, to diagnose the problems
associated with the biases in Tmin, we will start by examining the nighttime surface LWdn, lower- and
mid-upper tropospheric water vapor content (and the associated relative humidity and temperature), and
also cloud amount in the lower and mid-upper troposphere.
It quickly becomes clear that such a diagnostic analysis can be fairly complicated as many of the
processes are tightly coupled. For example, the nighttime warm biases at the surface and lower
troposphere will likely have an impact on the mid-upper tropospheric temperature and humidity fields,
thus the cause-and-effect relationship can become fuzzy. Nevertheless, there may be other deficient
physical processes (e.g., too much vertical transport of moisture by convection) that may lead to excessive
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water vapor and nighttime clouds in the mid-upper troposphere, or an excessively high tropopause over
the US so that a higher surface temperature can be maintained as the height of the effective radiative
equilibrium temperature is raised. Thus, the bias in the Tmin metric can only point to an excessive
nighttime greenhouse effect. Diagnosing what causes this excessive greenhouse effect will require
additional knowledge and information. We hope to gain in-depth understanding of the major controlling
factors for Tmin through our empirical analysis in Task 3.3.2, and apply this knowledge to diagnose the
causes for the Tmin and other diurnal biases in the GFDL and other models.
Since a climate model has consistent internal physics, a bias in one variable (such as a warm Tmin)
will likely be associated with consistent biases in other related fields (e.g., warm T and high specific
humidity q in the lower troposphere at night, and large LWdn). The challenge is to find the initial cause(s)
that triggered this train of biased response. The diagnostic analysis can only point to some possible causes.
The final solution will depend on the results from model test runs and may vary from model to model. We
will work with our GFDL collaborators to make some test runs to see if our diagnosed causes can really
reduce the diurnal biases in Tmin and other fields. For example, if we found that nighttime water vapor in
the mid-upper troposphere is too excessive over the US in the GFDL AM4, then we will work together to
find a best way to reduce this water vapor bias in the model. This will be a non-trivial task as the mid-
upper tropospheric water vapor is controlled by different processes, such as convection, large-scale
circulation, etc. The key to success for this kind of work is a close collaboration between the people doing
the diagnostic work and the people doing the modeling work. In this project, we have set up a good team
with the participation of two leading modelers from GFDL who have a strong desire to improve their
model.
3.4 Relevance to the MAPP Competition and NOAA CPO's long-term goal
This proposal is for MAPP Competition - Process-oriented evaluation of climate and Earth system
models and derived projections (Area A, Type 2), which emphasizes projects to "develop and apply
process-oriented metrics to evaluate simulated climate phenomena with strong theoretical and
observational bases". The diurnal cycle is a well-studied, fundamental feature of Earth's climate. The
focus of our diurnal metrics on the sub-daily processes and our emphasis on linking diurnal biases to
underlying physical processes make our metrics truly process-oriented. We will also apply the new
diurnal metrics to diagnose the simulation of the diurnal cycle in the GDFL and other models. Thus, this
proposal is directly responsive to the MAPP competition. Improving climate models and our
understanding of the diurnal cycle is also an important step to achieve NOAA's long-term climate goal to
"improved scientific understanding of the changing climate system and its impacts".
3.5 Work Plan
Year 1: To gather, quality-control, and compile a diurnal data set based on the data sources listed in
Table 1, quantify the diurnal variations in various fields (cf. Table 2) and start the analysis of the
underlying processes (Tasks 3.2.1 and 3.2.2);
Year 2: To complete the analysis of underlying physical processes, and then design and compute the
diurnal metrics and link them to specific physical processes (Task 3.2.3); and
Year 3: To apply the diurnal metrics to diagnose problems in GFDL AM4 and other CMIP5 models
(Task 3.2.4).
Comparisons with GFDL AM4 prototype model will be made from the start, so that some feedback
from this project will be available before GFDL freezes its next version of the AM4, although more
detailed diagnostic analyses will be in Year 3. Throughout the project, research results will be presented
at AMS, AGU and other conferences, and will be written up for publication.
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Table 2. Variables, associated diurnal metrics, and their underlying physical processes to be analyzed.
Area
Variable: Diurnal Metric Underlying Physical Processes or factors
Thermal
heating
Surface daily maximum air temperature
(Tmax): TmaxTmean
Surface solar radiation: surface and cloud albedo
Surface evaporation: soil moisture, wind speed, RH
Surface daily minimum air temperature
(Tmin): TmeanTmin
Tmean = daily mean temperature
Surface downward LW radiation: temperature, water
vapor, and cloud amount in the lower troposphere, which
is related to the greenhouse warming from the mid-upper
troposphere.
DTR (=Tmax-Tmin): DTR All the processes under Tmax and Tmin
Surface pressure (Ps): the amplitude
(A=maximum - daily mean) and
phase of the diurnal (24-hr, S1) and
semidiurnal (12-hr, S2) tides
S1: surface sensible heating, tropospheric latent and solar
heating.
S2: stratospheric ozone heating, tropospheric latent and
solar heating, spurious wave reflection at model top.
Surface sensible (SH) and latent (LH)
heat fluxes: diurnal amplitude (A)
and local time of maximum (tmax)
SH: surface Ts-Ta gradient, turbulence mixing, near-
surface wind speed, PBL1 stability
LH: soil moisture content, vegetation type, surface vapor
deficit, wind speed, and radiative heating
T, q, & RH in PBL: diurnal amplitude
(A) and local time of maximum (tmax)
at a level inside the PBL1
Related to the diurnal variations in surface temperature
(Ts), SH flux, and turbulence mixing in the PBL.
PBL height: diurnal amplitude (A)
and local time of maximum (tmax)
Related to diurnal variations in Ts, SH flux, and
turbulence mixing in the PBL
Tropospheric
Conditions
Air temperature (T), specific humidity
(q), relative humidity (RH): diurnal
amplitude (A) and local time of
maximum (tmax) at 850, 500 and
200hPa
Lower troposphere: related to surface T, SH and LH
diurnal variations and the T and SH diurnal cycle inside
the PBL
Mid to upper troposphere: related to atmospheric
absorption of solar radiation, latent heating.
Large-scale vertical velocity ():
diurnal amplitude (A) and local time
of maximum (tmax) at 850, 500 and
200hPa averaged over the time
without convection
Related to large-scale diurnal circulation (e.g., sea
breezes) and differential surface heating over land (e.g.,
between the Rockies and the central Great Plains in the
US, Fig. 4). May enhance or suppress local convection
depending on its diurnal phase.
Near-surface winds and low-level
convergence: diurnal amplitude (A)
and local time of maximum (tmax) for
wind and convergence fields at 10m
and 850hPa
Surface winds: related to the diurnal cycle of the PBL
(downward mixing of momentum);
Low-level convergence: related to convection and large-
scale vertical motion (e.g., the Great Plain low-level jet).
Clouds,
Convection and
Precipitation
Warm-seasonal low-level cloud amount
and top height: diurnal amplitude (A)
and local time of maximum (tmax)
Related to shallow and deep cumulus convection, which
in turn is related to surface sensible and latent heating,
and lower-tropospheric instability, large-scale motion,
etc.
CAPE2, CIN
3 (negative buoyancy),
convective precipitation amount,
frequency and intensity: diurnal
amplitude (A) and local time of
maximum (tmax)
CAPE and CIN: related to surface sensible and latent
heating and convection
Convective P frequency: related to frequency of moist
convection or the onset threshold of deep convection
Convective P intensity: related to intensity of deep
convection or amount of CAPE it consumes. 1 PBL = the Planetary Boundary Layer, the lowest atmospheric layer (~1km) with well mixed T and q
profiles. 2 CAPE = Convective Available Potential Energy
3 CIN = Convective Inhibition (i.e., the negative buoyancy below the level of free convection)
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3.6 Project Management and Collaborations
PI A. Dai will lead this project and be responsible for the implementation of the proposed work. He
will supervise the graduate students supported by this proposal and work with Co-PI J. Wang to carry out
most of the proposed work. Co-Is Chris Golaz and M. Zhao will share their expertise in studying and
modeling the PBL, clouds and convection during the diurnal analyses (Tasks 3.2.1-3.2.3), and provide
high temporal-resolution data from GFDL AM4 model simulations and help diagnose underlying
deficiencies in the GFDL model (Task 3.2.4).
3.7 References
Bernie, D. J., E. Guilyardi, G. Madec, J. M. Slingo, S. Woolnough, and J. Cole, 2008: Impact of resolving
the diurnal cycle in an ocean-atmosphere GCM. Part 2: A diurnally coupled CGCM. Clim. Dyn., 31,
909–925.
Ciesielski, P. E., H. Yu, R. H. Johnson, K. Yoneyama, M. Katsumata, C. N. Long, J. Wang and others,
2014: Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and
corrections. J. Atmos. Oceanic Technol., 31, 741-764.
Covey, C., A. Dai, D. Marsh, and R. S. Lindzen, 2011: The surface-pressure signature in atmospheric
tides in modern climate models. J. Atmos. Sci., 68: 495-514.
Covey, C., A. Dai, R. S. Lindzen, and D. Marsh, 2014: Atmospheric tides in the latest generation of
climate models. J. Atmos. Sci., 71, 1905-1913.
Dai, A., 2001: Global precipitation and thunderstorm frequencies. Part II: Diurnal variations. J. Climate,
14, 1112–1128.
Dai, A., 2006a: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 4605-
4630.
Dai, A., 2006b: Recent climatology, variability and trends in global surface humidity. J. Climate, 19,
3589-3606.
Dai, A. and C. Deser, 1999: Diurnal and semidiurnal variations in global surface wind and divergence
fields. J. Geophys. Res., 104, 31109-31125.
Dai, A. and J. Wang, 1999: Diurnal and semidiurnal tides in global surface pressure fields. J. Atmos. Sci.,
56, 3874-3891.
Dai, A., K. E. Trenberth, and T. R. Karl, 1999a: Effects of clouds, soil moisture, precipitation and water
vapor on diurnal temperature range. J. Climate, 12, 2451-2473.
Dai, A., F. Giorgi, and K. E. Trenberth, 1999b: Observed and model simulated precipitation diurnal cycle
over the contiguous United States. J. Geophys. Res., 104, 6377–6402.
Dai, A., J. Wang, R. H. Ware, and T. Van Hove, 2002: Diurnal variation in water vapor over North
America and its implications for sampling errors in radiosonde humidity. J. Geophys. Res., 107(D10),
4090, 10.1029/2001JD000642.
Dai, A., and K. E. Trenberth, 2004: The diurnal cycle and its depiction in the Community Climate System
Model. J. Climate, 17, 930–951.
Dai, A., X. Lin, and K.-L. Hsu, 2007: The frequency, intensity, and diurnal cycle of precipitation in
surface and satellite observations over low- and mid-latitudes. Climate Dyn., 29, 727–744.
Dai, A., J. Wang, P. W. Thorne, D. E. Parker, L. Haimberger, and X. L. Wang, 2011: A new approach to
homogenize daily radiosonde humidity data J. Climate, 24, 965-991.
Danabasoglu, G., W. G. Large, J. J. Tribbia, P. R. Gent, B. P. Briegleb, and J. C. McWilliams, 2006:
Diurnal coupling in the tropical oceans of CCSM3. J. Clim., 19, 2347–2365.
Delworth et al. 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation
characteristics. J. Climate. 19, 643-673.
![Page 17: Diurnal Metrics for Evaluating GFDL and Other Climate Modelsfunnel.sfsu.edu/students/luyilin/Lu_Yilin/Spring2015/YL/untitled... · NOAA Climate Program Office FY 2015 – NOAA-OAR-CPO-2015-2004099](https://reader033.fdocuments.in/reader033/viewer/2022060221/5f0739ff7e708231d41bf051/html5/thumbnails/17.jpg)
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Donner, L.J., and coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation
characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J.
Climate, 24, 3484–3519.
Du, Y. and R. Rotunno, 2014: A simple analytical model of the nocturnal low-level jet over the Great
Plains of the United States. J. Atmos. Sci., 71, 3674–3683.
Golaz, J.-C., M. Salzmann, L. J. Donner, L. W. Horowitz, Y. Ming, and M. Zhao, 2011: Sensitivity of the
aerosol indirect effect to subgrid variability in the cloud parameterization of the GFDL Atmosphere
General Circulation Model AM3. J. Climate, 24, 3145-3160, doi: 10.1175/2010JCLI3945.1.
Golaz, J.-C., L. W. Horowitz and H. Levy II, 2013: Cloud tuning in a coupled climate model: impact on
20th century warming. Geophys. Res. Lett., 40, 1-6, doi: 10.1002/grl.50232
Guo, H., J.-C. Golaz, L.J. Donner, P. Ginoux, and R.S. Hemler, 2014: Multivariate probability density
functions with dynamics in the GFDL Atmospheric General Circulation Model: Global tests. J.
Climate, 27, 2087–2108.
Higgins, R. W., J. E. Janowiak, and Y.-P. Yao, 1996: A gridded hourly precipitation data base for the
United States (1963-1993), NCEP/Climate Prediction Center Atlas No. 1, U.S. Dept. of Commerce,
Washington, D.C., 47pp.
Higgins, W., and coauthors, 2006: The NAME 2004 Field Campaign and Modeling Strategy. Bull. Amer.
Met. Soc., 87, 79-94.
Holtslag, A. A. M., and B. A. Boville, 1993: Local versus nonlocal boundary layer diffusion in a global
climate model, J. Clim., 6, 1825–1842.
Hourdin, F., et al., 2013: LMDZ5B: The atmospheric component of the IPSL climate model with
revisited parameterizations for clouds and convection. Clim. Dyn., 40, 2193–2222.
Huffman G.J., R.F. Adler, D.T. Bolvin, G.J. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F. Stocker, D.B.
Wolff, 2007: The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear,
combined-sensor precipitation estimates at fine scales. J Hydrometeorol. 8, 38–55
Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. J. Gu, 2009: Improving the global precipitation record:
GPCP version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.
Hsu, K.L., X.G. Gao, S. Sorooshian, H.V. Gupta, 1997: Precipitation estimation from remotely sensed
information using artificial neural networks. J Appl Meteorol 36, 1176–1190
Johnson, R. H., S. F. Williams, and P. E. Ciesielski, 2012: Legacy atmospheric sounding dataset project.
Bull. Amer. Meteor. Soc., 93, 14–17.
Johnson, R. H., P. E. Ciesielski, J. H. Ruppert, Jr., and M. Katsumata, 2014: Sounding-based
thermodynamic budgets for DYNAMO. J. Atmos. Sci., submitted.
Joyce, R.J., J.E. Janowiak, P.A. Arkin, and P.P. Xie, 2004: CMORPH: A method that produces global
precipitation estimates from passive microwave and infrared data at high spatial and temporal
resolution. J Hydrometeorol.. 5, 487–503.
Khairoutdinov, M. F., D. A. Randall, and C. DeMott, 2005: Simulations of the atmospheric general
circulation using a cloud-resolving model as a superparameterization of physical processes. J. Atmos.
Sci., 62, 2136–2154.
Liang, X.-Z., L. Li, A. Dai, K. E. Kunkel, 2004: Regional climate model simulation of summer
precipitation diurnal cycle over the United States. Geophys. Res. Lett., 31, L24208,
doi:10.1029/2004GL021054.
Lin, X., D.A. Randall, and L.D. Fowler, 2000: Diurnal variability of the hydrologic cycle and radiative
fluxes: Comparisons between observations and a GCM. J. Climate, 13, 4159-4179.
Lin,Y., M. Zhao, Y. Ming, J.-C. Golaz, L.J. Donner, S.A. Klein, V.Ramaswamy, and S. Xie, 2013:
Precipitation partitioning, rropical clouds, and intraseasonal variability in GFDL AM2. J. Climate, 26,
5453–5466.
Lindvall, J., G. Svensson, and C. Hannay, 2013: Evaluation of near-surface parameters in the two
versions of the atmospheric model in CESM1 using flux station observations. J. Clim., 26 26–44.
Lindvall, J. and G. Svensson, 2014: The diurnal temperature range in the CMIP5 models. Clim. Dyn. DOI
10.1007/s00382-014-2144-2, in press.
![Page 18: Diurnal Metrics for Evaluating GFDL and Other Climate Modelsfunnel.sfsu.edu/students/luyilin/Lu_Yilin/Spring2015/YL/untitled... · NOAA Climate Program Office FY 2015 – NOAA-OAR-CPO-2015-2004099](https://reader033.fdocuments.in/reader033/viewer/2022060221/5f0739ff7e708231d41bf051/html5/thumbnails/18.jpg)
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Menne, M.J., I, Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose,
B. E. Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-
Daily), Version 3. NOAA National Climatic Data Center. doi:10.7289/V5D21VHZ.
Ploshay, J.J., and N.-C. Lau, 2010: Simulation of the diurnal cycle in tropical rainfall and circulation
during boreal summer with a high-resolution GCM. Mon. Wea. Rev. 138, 3434-3453.
Pu, B. and R. E. Dickinson, 2014: Diurnal Spatial Variability of Great Plains Summer Precipitation
Related to the Dynamics of the Low-Level Jet. J. Atmos. Sci., 71, 1807–1817.
Randall, D. A., Harshvardhan, and D. A. Dazlich, 1991: Diurnal variability of the hydrologic cycle in a
general circulation model. J. Atmos. Sci., 48, 40–62.
Rossow, WB., RA Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor.
Soc. 80, 2261-2287.
Rossow, W.B., Tselioudis, G., Polak, A., and Jakob, C (2005), Tropical climate described as a
distribution of weather states indicated by distinct mesoscale cloud property mixtures. Geophys. Res.
Lett., 32, L21812, doi:10.1029/2005GL024584.
Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Q. Wang, 2009: Diurnal cycle of precipitation in
the Tropics simulated in a global cloud-resolving model. J. Clim., 22, 4809–4826.
Seidel, D. J., M. Free, and J. Wang, 2005: Diurnal cycle of upper-air temperature estimated from
radiosondes, J. Geophys. Res., 110, D09102, doi:10.1029/2004JD005526.
Seidel, D. J., Y. Zhang, A. Beljaars, J.-C. Golaz, A. R. Jacobson, and B. Medeiros, 2012: Climatology of
the planetary boundary layer over the continental United States and Europe, J. Geophys. Res., 117,
D17106, doi:10.1029/2012JD018143.
Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P.
Gabriel, and J. Haynes, 2010: Dreary state of precipitation in global models, J. Geophys. Res., 115,
D24211, doi:10.1029/2010JD014532.
Stratton, R. A., and A. J. Stirling, 2012: Improving the diurnal cycle of convection in GCMs. Q. J. R.
Meteorol. Soc., 138, 1121–1134.
Suzuki, K., J.-C. Golaz and G. L. Stephens, 2013: Evaluating cloud tuning in a climate model with
satellite observations. Geophys. Res. Lett., 40, 4464-4468, doi: 10.1002/grl.50874
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design.
Bull. Amer. Met. Soc., 93, 485-498.
Wang, B., H. J. Kim, K. Kikuchi, and A. Kitoh, 2011: Diagnostic metrics for evaluation of annual and
diurnal cycles. Clim. Dyn., 37, 941–955.
Wang, J., H.L. Cole, D.J. Carlson, E.R. Miller, K. Beierle, A. Paukkunen, and T.K. Laine, 2002:
Corrections of humidity measurement errors from the Vaisala RS80 radiosonde–Application to
TOGA_COARE data. J. Atmos. Oceanic Technol., 19, 981-1002.
Wang, J., D. J. Carlson, D. B. Parsons, T. F. Hock, D. Lauritsen, H. L. Cole, K. Beierle and N.
Chamberlain, 2003: Performance of operational radiosonde humidity sensors in direct comparison
with a chilled mirror dew-point hygrometer and its climate implication. Geophy. Res. Lett., 30,
10.1029/2003GL016985.
Wang, J., L. Zhang, A. Dai, T. Van Hove, J. Van Baelen, 2007: A near-global, 2-hourly data set of
atmospheric precipitable water from ground-based GPS measurements. J. Geophys. Res., 112,
D11107. 10.1029/2006JD007529.
Wang, J. and L. Zhang, 2008: Systematic errors in global radiosonde precipitable water data from
comparisons with ground-based GPS measurements. J. Climate, 21, 2218-2238.
Wang, J., and L. Zhang, 2009: Climate applications of a global, 2-hourly atmospheric precipitable water
dataset from IGS ground-based GPS measurements. J. Geodesy, 83, 209-217.
Wang, J., L. Zhang, P.-H. Lin, Mark Bradford, Harold Cole, Jack Fox, Terry Hock, Dean Lauritsen, Scot
Loehrer, Charlie Martin, Joseph VanAndel, Chun-Hsiung Weng and Kathryn Young, 2010: Water
vapor variability and comparisons in subtropical Pacific from T-PARC Driftsonde, COSMIC and
reanalyses. J. Geophys. Res., 115, D21108, doi:10.1029/2010JD014494.
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Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer and H. Voemel, 2013a: Radiation dry bias correction
of Vaisala RS92 humidity data and its impacts on historical radiosonde data. J. Atmos. Oceanic
Technol., 30, 197-214.
Wang, J., T. Hock, S. A. Cohn, C. Martin, N. Potts, T. Reale, B. Sun and F. Tilley, 2013b: Unprecedented
upper air dropsonde observations over Antarctica from the 2010 Concordiasi Experiment: Validation
of satellite-retrieved temperature profiles. Geophys. Res. Lett., 40, DOI: 10.1002/grl.50246.
Wang, J., A. Dai, C. Mears and L. Zhang, 2014: Global water vapor trend and its diurnal asymmetry
based on GPS, radiosonde and microwave satellite measurements. J. Geophys. Res., to be submitted.
Ware, R.H. D. W. Fulker, S. A. Stein, D. N. Anderson, S. K. Avery, R. D. Clark, K. Droegemeier, J. P.
Kuettner, and J. B. Minster, 2000: Suominet: A real-time national GPS network for atmospheric
research and education. Bull. Amer. Meteor. Soc. 81, 677-694.
Webster, P.J., and R.Lukas, 1992: TOGA COARE: The Coupled Ocean-Atmosphere Response
Experiment. Bull. Amer. Meteor. Soc., 73, 1377-1416.
Weckwerth, T. M, D. B. Parsons, S. E. Koch, J. A. Moore, M. A. LeMone, B. B. Demoz, C. Flamant, B.
Geerts, J. Wang and W. F. Feltz, 2004: An overview of the international H20 project (IHOP_2002)
and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253-277.
Yoneyama, K. C. Zhang, and C.N. Long, 2013: Tracking Pulses of the Madden–Julian Oscillation. Bull.
Amer. Meteor. Soc., 94, 1871–1891.
Xie, S. and coauthors, 2010: Clouds and more: ARM Climate Modeling Best Estimate Data. Bull. Amer.
Meteor. Soc., 91, 13–20.
Zhang, Y., D.J. Seidel, J.-C. Golaz, C. Deser, and R. A. Tomas, 2011: Climatological characteristics of
Arctic and Antarctic surface-based inversions. J. Climate, 24, 5167–5186.
Zhang, Y.-C., W. B. Rossow, and A. A. Lacis, 1995: Calculation of surface and top of atmosphere
radiative fluxes from physical quantities based on ISCCP data sets. Part 1: Methods and sensitivity to
input data uncertainties. J. Geophys. Res., 100, 1149–1165.
Zhao, M., I. M. Held, S.-J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology,
interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate,
22, 6653–6678, doi:10.1175/2009JCLI3049.1.
Zhao, M. 2014: An investigation of the connections among convection, clouds, and climate sensitivity in
a global climate model. J. Climate, 27, 1845–1862.
Zhao, T., A. Dai, and J. Wang, 2012: Long-term trend of upper-air humidity over China from
homogenized radiosonde data. J. Climate, 25, 4549-4567.
4. Data/Information Sharing Plan:
The results and data produced in this project, including the diurnal data set, the diurnal
metrics data, and the information regarding the underlying physical processed, will be made
available to the public through presentations at meetings, publications in journals, and online
postings. In particular, we will work with any modeling groups for them to include our diurnal
metrics and related data sets into their model evaluation packages.
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6. Curriculum Vitae:
Biographical Sketch Aiguo Dai Associate Professor
Depart. of Atmos. and Environ. Sci., University at Albany, SUNY
Tel. 518-442-4474; email: [email protected]
A. Professional Preparation: Postdoc: 1997-98, Climate & Global Change, NCAR, Boulder, CO
Ph.D.: 1996, Atmospheric Science, Columbia University, NY, NY
M.S.: 1988, Atmospheric Science, Chinese Academy of Sci., Beijing, China
B.S.: 1985, Meteorology, Nanjing University, Nanjing, China
B. Appointments: 8/2012-present: Associate Professor, Dept of Atmos. & Environ. Sci., SUNY, Albany, NY
7/2008-7/2012: Scientist III, CGD, NCAR, Boulder
9/2004-6/2008: Scientist II, CGD, NCAR, Boulder
1999-8/2004: Project Scientist, CGD, NCAR, Boulder, CO.
1997-1998: NOAA/UCAR Postdoctoral Scientist at NCAR, Boulder, CO.
C. Synergistic Activities (selected):
Editor, J. Climate, January 2011-present
Chair, AMS Committee on Climate Variability and Change, January 2011-February 2014
Member, AMS Committee on Climate Variability and Change, Feb. 2006 – Jan. 2011
Associate Editor, J. Hydrology, January 2010-present
Associate Editor, Advances in Atmospheric Sciences, Oct. 2009-August 2011
Chair, AMS 20th Conf. on Climate Variability and Change, 20–24 January 2008, New Orleans
Contributing Author, the 3rd
, 4th and 5
th IPCC Assessment Reports
D. Journal Publications Since 2011 (see www.cgd.ucar.edu/cas/adai/pub.html for links to pdf files)
Total citations 10,839, H index=49 as of October 2014 based on Google Scholar. One of the ~3200
highly cited researchers in all fields (one of the 159 in Geoscience) in the world.
26. Dong, B., and A. Dai, 2014: The influence of the Inter-decadal Pacific Oscillation on temperature and
precipitation over the globe. Climate Dynamics, being revised.
25. Hegerl, G.C., and 26 Co-authors (including A. Dai), 2014: Challenges in quantifying changes in the global
water cycle. Bull. Am. Met. Soc., accepted.
24. Zhao, T., and A. Dai, 2014: The magnitude and causes of global drought changes in the 21st century under
a moderate emissions scenario. J. Climate, being revised.
23. Dai, A., J.C. Fyfe, S.-P. Xie, and X. Dai, 2014: Decadal modulation of global-mean temperature by
internal climate variability. Nature Climate Change, under review.
22. Luo, D., Y. Yao*, and A. Dai, 2014: Decadal relationship between the European blocking and North
Atlantic Oscillation. Part I: Atlantic conditions. J. Atmos. Sci., accepted with minor revision.
21. Luo, D., Y. Yao*, and A. Dai, 2014: Decadal relationship between the European blocking and North
Atlantic Oscillation. Part II: A theoretical model study. J. Atmos. Sci., accepted with minor.
20. Covey, C., A. Dai, R. S. Lindzen, and D. Marsh, 2014: Atmospheric tides in the latest generation of
climate models. J. Atmos. Sci., 71, 1905-1913.
19. Rasmussen, R., K. Ikeda, C. Liu, D. Gochis, M. Clark, A. Dai, E. Gutmann, J. Dudhia, F. Chen, M.
Barlage, and D. Yates, 2014: Impacts of climate change on the water balance of the Colorado headwaters:
High resolution regional climate model simulations. J. Hydrometeorol., doi: 10.1175/JHM-D-13-0118.1.
18. Luo, D., J. Cha*, L. Zhong*, and A. Dai, 2014: A nonlinear multi-scale interaction model for atmospheric blocking: The eddy-blocking matching mechanism. Q. J. Roy.Meteorol. Soc., DOI:10.1002/qj.2337.
17. Trenberth, K.E., A. Dai, G. van der Schrier, P.D. Jones, J. Barichivich*, K.R. Briffa, and J. Sheffield,
2014: Global warming and changes in drought. Nature Climate Change, 4, 17-22.
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16. Dai, A., H. Li, Y. Sun, L.-C. Hong*, LinHo, C. Chou, and T. Zhou, 2013: The relative roles of upper and
lower tropospheric thermal contrasts and tropical influences in driving Asian summer monsoons. J . Geophys. Res., 118: 7024-7045, doi:10.1002/jgrd.50565.
15. Jia, B., Z. Xie, A. Dai, C. Shi, and F. Chen*, 2013: Evaluation of satellite and reanalysis products of
downward surface solar radiation over East Asia: Spatial and seasonal variations. J. Geophys. Res.-
Atmospheres, 118: doi:10.1002/jgrd.50353.
14. Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer, and H. Vömel, 2013: Radiation dry bias correction of
Vaisala RS92 humidity data and its impacts on historical radiosonde data. J. Atmos. Oceanic Tech. 30:
197-214.
13. Dai, A., 2013: The Influence of the Inter-decadal Pacific Oscillation on U.S. precipitation during 1923-
2010. Climate Dynamics, 41: 633-646, doi:10.1007/s00382-012-1446-5.
12. Dai, A., 2013: Increasing drought under global warming in observations and models. Nature Climate Change, 3: 52-58.
11. Shiu, C.-J., S.C. Liu, S.C, C. Fu, A. Dai and Y. Sun, 2012: How much do precipitation extremes change in
a warming climate? Geophys. Res. Lett. 39, L17707, doi:10.1029/2012GL052762.
10. Dunn, R.J.H., K.M. Willett, P.W. Thorne, E. V. Woolley, I. Durre, A. Dai, D. E. Parker, and R.S. Vose,
2012: HadISD: A Quality Controlled global synoptic report database for selected variables at long-term
stations from 1973-2010. Climates of the Past. 8, 1649–1678, doi: 10.5194/cp-8-1649-2012.
9. Bacmeister, J. T., P. H. Lauritzen, A. Dai and J. E. Truesdale, 2012: Assessing possible dynamical effects of
condensate in high resolution climate simulations. Geophys. Res. Lett., 39, L04806,
doi:10.1029/2011GL050533.
8. Zhao, T., A. Dai, and J. Wang, 2012: Trends in tropospheric humidity from 1970-2008 over China from a
homogenized radiosonde dataset. J. Climate, 25: 4549-4567.
7. Dai, A., 2011: Characteristics and trends in various forms of the Palmer Drought Severity Index (PDSI)
during 1900-2008. J. Geophys. Res., 116, D12115, doi:10.1029/2010JD015541.
6. Willett, K., A. Dai, and D. Berry, 2011: Surface humidity. In: State of the Climate in 2010. Bull. Amer.
Meteorol. Soc., 92, S40-S41.
5. Wang, L., Q. Huang, A. Dai, Z. Guan, J. He, and Z. Wu, 2011: Inhomogeneous distributions of Meiyu
rainfall in the Jiang-Huai basin, and associated circulation patterns. Climate Res., 50, 203-214.
4. Monahan, A., Y. He, N. McFarlane, and A. Dai, 2011: The probability distribution of land surface wind
speeds. J. Climate, 24, 3892-3909.
3. Dai, A., J. Wang, P.W. Thorne, D.E. Parker, L. Haimberger, and X.L. Wang, 2011: A new approach to
homogenize daily radiosonde humidity data. J. Climate, 24, 965-991.
2. Covey, C., A. Dai, D. March, and R. S. Lindzen, 2011: The surface-pressure signature of atmospheric tides
in modern climate models. J. Atmos. Sci., 68, 495-514.
1. Dai, A., 2011: Drought under global warming: A review. Wiley Interdisciplinary Reviews: Climate Change,
2, 45-65.
Five other relevant papers:
Dai, A., X. Lin, and K.-L. Hsu, 2007: The frequency, intensity, and diurnal cycle of precipitation in surface
and satellite observations over low- and mid-latitudes. Climate Dynamics, 29, 727-744.
Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 4605-4630.
Dai, A., and K. E. Trenberth, 2004: The diurnal cycle and its depiction in the Community Climate System
Model. J. Climate, 17, 930-951.
Dai, A., J. Wang, R. H. Ware, and T. Van Hove, 2002: Diurnal variation in water vapor over North America
and its implications for sampling errors in radiosonde humidity. J. Geophys. Res., 107(D10), 4090,
10.1029/2001JD000642.
Dai, A., K. E. Trenberth, and T. R. Karl, 1999: Effects of clouds, soil moisture, precipitation and water vapor
on diurnal temperature range. J. Climate, 12, 24512473.
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Biographical Sketch – Junhong (June) Wang, Ph.D. Research Associate Professor
Department of Atmospheric and Environmental Sciences
University at Albany, SUNY
1400 Washington Avenue, Albany, NY 12222
Tel. 518-442-3478; email: [email protected]
A. Professional Preparation Peking University, Beijing, China Atmospheric Science B.S. 1987
NRCMEF, Beijing, China Atmospheric Science M.S. 1990
(National Research Center for Marine Environment Forecasts)
Columbia University, New York Atmospheric Science Ph.D. 1997
University of Colorado, Boulder, CO Atmospheric Science PostDoc 1997-1998
B. Appointments
2012-present Research Associate Professor, Department of Atmospheric & Environmental
Sciences University at Albany, SUNY, Albany, NY
2008-2014 Scientist III, Earth Observing Laboratory (EOL), NCAR, Boulder, CO
6/2009-8/2009 Visiting Scientist, U.K. Met Office Hadley Centre, Exeter, U.K.
2005-2008 Scientist II, EOL, NCAR, Boulder, CO
2002-2005 Scientist I, Atmospheric Technology Division (ATD), NCAR, Boulder, CO
1999-2001 Associate Scientist, ATD, NCAR, Boulder, CO
1997-1998 Postdoctoral Research Scientist, University of Colorado, Boulder, CO
1991-1996 Graduate Research Assistant, NASA Goddard Institute for Space Studies (GISS), NY
1993-1994 Teaching Assistant for Atmospheric & Climate Courses, Columbia Univ.
6/1991-9/1991 Research Assistant, NASA GISS, New York
7/1990-12/1990 Research Associate, NRCMEF/Peking University, China
1988-1990 Graduate Research Assistant, NRCMEF/Peking Univ., China
C. Publications
i) Publications of the last 3 years: 1. Mears, C. A., J. Wang, D. Smith and F. J. Wentz, 2014: Intercomparison of total precipitable water
measurements made by satellite borne microwave radiometers and ground-based GPS instruments. J. Geophys. Res., submitted.
2. Boylan, P., J. Wang, S. Cohn, E. Fetzer, E. S. Maddy, and S. Wong, 2014: Validation of AIRS version 6
temperature profiles and surface-based inversions over Antarctica using Concordiasi dropsonde data. J.
Geophys. Res., submitted.
3. Wang, J., K. Young, T. Hock, D. Lauritsen, D. Behringer, M. Black, P. G. Black, J. Franklin, J.
Halverson, J. Molinari, L. Nguyen, T. Reale, J. Smith, B. Sun, Q. Wang and J. Zhang, 2014: A long-term,
high-quality, high vertical resolution GPS dropsonde dataset for hurricane and other studies. Bull. Amer. Meteor. Soc., accepted.
4. Mears, C, S. Ho, L. Peng and J. Wang, 2014: Total column water vapor, in State of the Climate in 2013.
Bull. Amer. Meteorol. Soc., 95, S20-21.
5. Yu, H., P.E. Ciesielski, J. Wang, H.-C. Kuo, H. Voemel and R. Dirksen, 2014: Evaluation of humidity
correction methods for Vaisala RS92 tropical sounding data. J. Atmos. Oceanic Technol., submitted.
6. Intrieri, J.M., G. de Boer, M.D. Shupe, J.R. Spackman, J. Wang, P.J. Neiman, G.A. Wick, T.F. Hock, and
R.E. Hood, 2014: Global Hawk dropsonde observations of the Arctic atmosphere during the Winter
Storms and Pacific Atmospheric Rivers (WISPAR) field campaign. Atmospheric Measurement Techniques, revised.
7. Bock, O., P. Willis, J. Wang and C. Mears, 2014: A high-quality, consistent, global, long-term (1993-
2008) DORIS precipitable water dataset for climate monitoring and model verification. J. Geophys. Res., accepted.
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8. Ciesielski, P. E., H. Yu, R. H. Johnson, K. Yoneyama, M. Katsumata, C. N. Long, J. Wang and others,
2014: Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and
corrections. J. Atmos. Oceanic Technol., 31, 741-764.
9. Mears, C., J. Wang, S. Ho, and L. Zhang, 2013: Total column water vapor, in State of the Climate in
2012. Bull. Amer. Meteorol. Soc., 94, S20-21.
10. Wang, J., T. Hock, S. A. Cohn, C. Martin, N. Potts, T. Reale, B. Sun and F. Tilley, 2013b: Unprecedented
upper air dropsonde observations over Antarctica from the 2010 Concordiasi experiment: Validation of
satellite-retrieved temperature profiles. Geophys. Res. Lett., 40, DOI: 10.1002/grl.50246.
11. Cohn, S. A., T. Hock, P. Cocquerez, J. Wang and others, 2013: Driftsondes: Providing in-situ long-
duration dropsonde observations over remote regions. Bull. Amer. Meteor. Soc., 94, 10.1175/BAMS-D-12-00075.1.
12. Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer and H. Voemel, 2013a: Radiation dry bias correction
of Vaisala RS92 humidity data and its impacts on historical radiosonde data. J. Atmos. Oceanic Technol.,
30, 197-214.
13. Rabier, F., S. Cohn, P. Cocquerez, A. Hertzog, L. Avallone, T. Deshler, J. Haase, T. Hock, A.
Doerenbecher, J. Wang, and others, 2013: The Concordiasi field experiment over Antarctica: first results
from innovative atmospheric measurements, Bull. Amer. Meteor. Soc., 94, DOI:10.1175/BAMS-D-12-
00005.1.
14. Mears, C., J. Wang, S. Ho, L. Zhang and X. Zhou, 2012: Total column water vapor, in State of the
Climate in 2011. Bull. Amer. Meteorol. Soc., 93, S25-S26.
15. Zhao, T., A. Dai, and J. Wang, 2012: Long-term trend of upper-air humidity over China from
homogenized radiosonde data. J. Climate, 25, 4549-4567.
16. Ciesielski, P. E., P. T. Haertel, R. H. Johnson, J. Wang, and S. M. Loehrer, 2012: Developing high-quality
field program sounding datasets. Bull. Amer. Meteorol. Soc., 93, 325–336.
17. Mears, C., J. Wang, S. Ho, L. Zhang and X. Zhou, 2011: Total column water vapor, in State of the
Climate in 2010. Bull. Amer. Meteorol. Soc., 92 (6), S41-S42.
18. Dai, A., J. Wang, P. W. Thorne, D. E. Parker, L. Haimberger, and X. L. Wang, 2011: A new approach to
homogenize daily radiosonde humidity data J. Climate, 24, 965-991.
ii) Five other relevant papers: 1. Wang, J., and L. Zhang, 2009: Climate applications of a global, 2-hourly atmospheric precipitable water
dataset from IGS ground-based GPS measurements. J. of Geodesy, 83, 209-217.
2. Wang, J. and L. Zhang, 2008: Systematic errors in global radiosonde precipitable water data from
comparisons with ground-based GPS measurements. J. Climate, 21, 2218-2238.
3. Wang, J., L. Zhang, A. Dai, T. Van Hove, J. Van Baelen, 2007: A near-global, 2-hourly data set of
atmospheric precipitable water from ground-based GPS measurements. Journal of Geophysical Research,
112, D11107. 10.1029/2006JD007529.
4. Seidel, D. J., M. Free and J. Wang, 2005: The diurnal cycle of temperature in the free atmosphere
estimated from radiosondes. J. Geophys. Res., 110, D09102, doi:10.1029/2004JD005526.
5. Dai, A., J. Wang, R. H. Ware, and T. M. Van Hove, 2002: Diurnal variation in atmospheric water vapor
over North America and its implications for sampling errors in radiosonde humidity. J. Geophys. Res.,
107(D10), 10.1029/2001JD000642.
D. Synergistic Activities (five examples)
• 2011-present: Member of AMS Board on Data Stewardship.
• 2010-present: Co-Chair for GRUAN task team on ground-based GNSS precipitable water (GNSS-PW)
observations.
• 2006-present: Member of Working Group on Atmospheric Reference Observations of GCOS
Atmospheric Observation Panel for Climate.
• 2005-2009: Editor for Journal of Atmospheric and Oceanic Technology.
• 2005: Member of NRC’s committee on reviewing US CCSP's first report about "Temperature trends in
the lower atmosphere: steps for understanding and reconciling differences"
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Biographical Sketch – Jean-Christophe “Chris” Golaz
Physical Scientist
NOAA Geophysical Fluid Dynamics Laboratory
201 Forrestal Rd ● Princeton, NJ 08540
Relevant experience
Chris Golaz is currently co-lead of the GFDL Atmospheric Working Group in charge of
developing the next generation GFDL atmospheric model (AM4). He is experienced in the
development of cloud parameterizations, including their implementation and evaluation in global
climate models.
Education
B.S. Physics, Swiss Federal Institute of Technology, Lausanne, Switzerland.
M.S. Atmospheric Science, Colorado State University.
PhD. Atmospheric Science, Colorado State University.
Selected publications
Guo, H., J.-C. Golaz, L. J. Donner, P. Ginoux and R. S. Hemler, 2014: Multivariate probability
density functions with dynamics in the GFDL atmospheric General Circulation Model:
Global tests. J. Clim., 27, 2087-2108, doi: 10.1175/JCLI-D-13-00347.1
Zhang, M. and co-authors, 2013: CGILS: Results from the first phase of an international project
to understand the physical mechanisms of low cloud feedbacks in single column models. J.
Adv. Model. Earth Syst., 5, 826-842, doi: 10.1002/2013MS000246
Suzuki, K., J.-C. Golaz and G. L. Stephens, 2013: Evaluating cloud tuning in a climate model
with satellite observations. Geophys. Res. Lett., 40, 4464-4468, doi: 10.1002/grl.50874
Lin, Y., M. Zhao, Y. Ming, J.-C. Golaz, L. J. Donner, S. A. Klein, V. Ramaswamy and S. Xie,
2013: Precipitation partitioning, tropical clouds, and intraseasonal variability in GFDL
AM2. J. Climate, 26, 5453-5466, doi: 10.1175/JCLI-D-12-00442.1
Golaz, J.-C., L. W. Horowitz and H. Levy II, 2013: Cloud tuning in a coupled climate model:
impact on 20th century warming. Geophys. Res. Lett., 40, 1-6, doi: 10.1002/grl.50232
Levy II, H., L. W. Horowitz, D. M. Schwarzkopf, Y. Ming, J.-C. Golaz, V. Naik and V.
Ramaswamy, 2013: The roles of aerosol direct and indirect effects in past and future climate
change. J. Geophys. Res., 118, 1-12, doi: 10.1002/jgrd.50192
Huang, J., E. Bou-Zeid and J.-C. Golaz, 2013: Turbulence and vertical fluxes in the stable
atmospheric boundary layer. Part II: a novel mixing-length model. J. Atmos. Sci., 70, 1528-
1542, doi: 10.1175/JAS-D-12-0168.1
Zhang, M. H., C. S. Bretherton, P. N. Blossey, S. Bony, F. Brient and J.-C. Golaz, 2012: The
CGILS experimental design to investigate low cloud feedbacks in general circulation models
by using single-column and large-eddy simulation models. J. Adv. Model. Earth Syst., 4,
M12001, doi: 10.1029/2012MS000182
Seidel, D. J., Y. Zhang, A. Beljaars, J.-C. Golaz, A. R. Jacobson, B. Medeiros, 2012:
Climatology of the planetary boundary layer over the continental United States and Europe.
J. Geophys. Res., 117, D17106, doi: 10.1029/2012JD018143.
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Zhang, Y., D. J. Seidel, J.-C. Golaz, C. Deser, and R. A. Tomas, 2011: Climatological
characteristics of Arctic and Antarctic surface-based inversions. J. Climate, 24, 5167-5186,
doi: 10.1175/2011JCLI4004.1.
Golaz, J.-C., M. Salzmann, L. J. Donner, L. W. Horowitz, Y. Ming, and M. Zhao, 2011:
Sensitivity of the aerosol indirect effect to subgrid variability in the cloud parameterization
of the GFDL Atmosphere General Circulation Model AM3. J. Climate, 24, 3145-3160, doi:
10.1175/2010JCLI3945.1.
Guo, H., J.-C. Golaz, and L. J. Donner, 2011: Aerosol effects on stratocumulus water paths in a
PDF-based parameterization. Geophys. Res. Lett., 38, L17808, doi: 10.1029/2011GL048611.
Donner, L. J. and co-authors, 2011: The dynamical core, physical parameterizations, and basic
simulation characteristics of the atmospheric component of the GFDL Global Coupled
Model CM3. J. Climate, 24, 3484-3519, doi: 10.1175/2011JCLI3955.1.
Stephens, G. L., T. L'Ecuyer, R. Forbes, A. Gettelmen, J.-C. Golaz, A. Bodas-Salcedo, K.
Suzuki, P. Gabriel and J. Haynes, 2010: Dreary state of precipitation in global models. J.
Geophys. Res, 115, D24211, doi: 10.1029/2010JD014532.
Guo, H., J.-C. Golaz, L. J. Donner, V. E. Larson, D. P. Schanen and B. M. Griffin, 2010: Multi-
variate probability density functions with dynamics for cloud droplet activation in large-
scale models: single column tests. Geosci. Model Dev., 3, 475-486, doi: 10.5194/gmd-3-
475-2010
Salzmann, M., Y. Ming, J.-C. Golaz, P. A. Ginoux, H. Morrisson, A. Gettelman, M. Krämer and
L. J. Donner, 2010: Two-moment bulk stratiform cloud microphysics in the GFDL AM3
GCM: description, evaluation, and sensitivity tests. Atmos. Chem. Phys., 10, 8037-8064, doi:
10.5194/acp-10-8037-2010
Xie, S., R. B. McCoy, S. A. Klein, R. T. Cederwall, W. J. Wiscombe, E. E. Clothiaux, K. L.
Gaustad, J.-C. Golaz, S. D. Hall, M. P. Jensen, K. L. Johnson, Y. Lin, C. N. Long, J. H.
Mather, R. A. McCord, S. A. McFarlane, G. Palanisamy, Y. Shi, and D. D. Turner, 2010:
CLOUDS AND MORE: ARM Climate Modeling Best Estimate Data. Bull. Amer. Meteor.
Soc., 91, 1, 13-20, doi: 10.1175/2009BAMS2891.1
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Ming Zhao CURRICULUM VITAE
Project Scientist III, University Corporation for Atmospheric Research
Geophysical Fluid Dynamics Laboratory / NOAA
Princeton University Forrestal Campus / 201 Forrestal Rd, Princeton, NJ 08540
Research Interests
Moist convection, clouds, atmospheric boundary layer turbulence and their
parameterizations in global climate models
Tropical cyclone and climate
Convection-clouds-climate-connections and feedback
Weather and climate extremes in changing climate
Education
Ph.D. in Atmospheric Sciences, University of British Columbia, Canada, 2003
M.Sc. in Atmospheric Remote Sensing, Nanjing Institute of Meteorology, China, 1993
B.Sc. in Atmospheric Physics, Nanjing Institute of Meteorology, China, 1990
Relevant Publications
1. Xiang, B., S-J Lin, M. Zhao, S. Zhang, G. Vecchi, T. Li, X. Jiang, L. Harris, J-H Chen,
2014: Beyond Weather Time Scale Prediction for Hurricane Sandy and Super Typhoon
Haiyan in a Global Climate Model. Mon. Wea. Rev. In Press.
2. Camargo, S.J., M.K. Tippett, A.H. Sobel, G.A. Vecchi, M. Zhao and I.M. Held, 2014:
Testing the performance of tropical cyclone genesis indices in future climates using the
HIRAM model. J. Climate. In Press.
3. Zhang, S., M. Zhao, S-J Lin, X. Yang and W. Anderson, 2014: Retrieval of Tropical
Cyclone Statistics with a High-Resolution Coupled Model and Data. Geophys. Res. Lett.,
41(2), DOI:10.1002/2013GL058879.
4. Zhao, M, 2014: An Investigation of the Connections between Convection, Clouds and
Climate Sensitivity in a Global Climate Model. J. Climate. 27, 1845-1862.
5. Jiang, X., M. Zhao and D. E. Waliser, 2012: Modulation of Tropical Cyclones Over the
Eastern Pacific by the Intra-seasonal Variability Simulated in an AGCM, J. Climate. 25,
6524-6538.
6. Zhao, M., I. M. Held and S-J Lin, 2012: Some Counter-Intuitive Dependencies of
Tropical Cyclone Frequency on Parameters in a GCM. J. Atmos. Sci., 69, 2272-2283.
7. Jiang, X., B.E., Waliser, D. Kim, M. Zhao, M. Khairoutdinov, W. Stern, S.D. Schubert,
K.R. Sperber, G.J. Zhang, W. Wang, R. Neale and M-I Lee, 2012: Simulation of the
Intraseasonal Variability over the Eastern Pacific ITCZ in Climate Models. Climate
Dynamics. 39, 617-636.
8. Zhao, M. and I. M. Held, 2012: TC-permitting GCM Simulations of Hurricane
Frequency Response to Sea Surface Temperature Anomalies Projected for the Late 21st
Century. J. Climate. 25, 2995-3009.
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9. Donner, L.J., B.L. Wyman, R.S. Hemler, L.W. Horowitz, Y. Ming, M. Zhao and 20 co-
authors, 2011: The Dynamical Core, Physical Parameterizations, and Basic Simulation
Characteristics of the Atmospheric Component of the GFDL Global Coupled Model
CM3. J. Climate, 24, 3484-3519.
10. Vecchi, G.A., M. Zhao, H. Wang, G. Villarini, A. Rosati, A. Kumar, I.M. Held and R.
Gudgel, 2011: Statistical-Dynamical Predictions of Seasonal North Atlantic Hurricane
Activity. Mon. Wea. Rev., 139, 1070-1082
11. Zhao, M. and I. M. Held, 2010: An Analysis of the Effect of Global Warming on the
Intensity of Atlantic Hurricanes Using a GCM with Statistical Refinement. J. Climate,
23, 6382–6393.
12. Zhao, M., I.M. Held and G.A. Vecchi, 2010: Retrospective Forecasts of the Hurricane
Season Using a Global Atmospheric Model Assuming Persistence of SST Anomalies.
Mon. Wea. Rev., 138, 3858-3868.
13. Zhao, M., I.M. Held, S-J. Lin, and G.A. Vecchi, 2009: Simulations of Global Hurricane
Climatology, Interannual Variability, and Response to Global Warming Using a 50km
Resolution GCM. J. Climate, 33, 6653-6678.