Funding Opportunities & Funded Projects

FY21 Research Opportunities

For both competitions, New Climate Monitoring Approaches and Products for Areas of Climate Risk, and Process-Oriented Diagnostics for Climate Model Improvement and Applications

LOIs are due August 17, 2020 by 5pm and Full Proposals are due November 30, 2020 by 5pm.

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Linkage Between Deep Convection, Large-scale Circulation and Low Cloud Feedback

It is well established that shortwave cloud feedback associated with marine boundary layer cloud (MBLC) fraction change is the primary contributor to the uncertainty in equilibrium climate sensitivity (ECS). Su et al. (2014) showed a metric that represents the model performance in capturing the spatial structures of zonal-mean cloud fraction and relative humidity associated with the Hadley Circulation can be used to constrain ECS. Qu et al. (2018) confirmed that the Su metric is correlated with the low cloud feedback. The Su metric is already in use by some groups testing Coupled Model Intercomparison Project Phase 6 (CMIP6) models, but process-oriented diagnostics that identify steps in the underlying physical pathways require development.

Work Summary: The proposed Type 1 project aims to establish the physical processes that link
deep convection, the Hadley Circulation and low cloud feedback, develop process-oriented model diagnostics to characterize CMIP6 model representation of these pathways, and apply multiple observations to constrain these pathways so as to constrain ECS. Based on existing knowledge and a set of perturbed physical experiments (PPEs) (e.g., Schiro et al. 2019) in which deep convective parameters were altered, we hypothesize three potential pathways for deep convection and large-scale circulation to modify low cloud fraction (LCF) change in a warmer climate: (1) the temperature-stability pathway that hinges on the tropospheric temperature anomalies propagated by wave dynamics, (2) the moisture-mixing pathway that may depend on shallow ascent and subgrid-scale mixing of moisture between the free troposphere and the MBL, and (3) the radiation stability pathway that involves longwave radiation mediated subsidence control on LCF. We will analyze CMIP6 simulations to determine the relative contribution of each pathway to the model spread in ECS and use satellite observations and reanalysis datasets to assess CMIP6 model representations of these processes and develop diagnostic tools to trace the sources of model errors.
A statistical hierarchical framework will be employed to estimate the probability of ECS given
present-day climate simulations of the physical pathways and their observations following Bayes’ theorem (Bowman et al. 2018). In addition, we will employ the Pareto-optimal technique to place multi-objective constraints on ECS (Langenbrunner and Neelin 2017a; 2017b) and compare the results from the two methods. This proposed work builds upon the strong expertise of the proposal team on model-observation diagnostics and evaluations of climate models using satellite observations especially related to deep convection and its parameterizations. We will leverage the process-oriented diagnostics approach and framework in the MAPP Model Diagnostics Task Force (MDTF) and contribute new diagnostics to augment the existing software package, helping to accelerate model improvements and reduce the uncertainties in climate projections. Relevance to competition: The proposed study targets the intimate linkage between deep convection and low cloud feedback through large-scale circulation and addresses one of the grand challenges put forward by the World Climate Research Programme on “Clouds, Circulation and Climate Sensitivity”. It is aligned with the MAPP solicitation as one of the “investigations to constrain climate model sensitivity focusing on clouds, convection and aerosol processes and their role within the coupled Earth system”. It will “develop key process-level metrics and diagnostics using relevant observations to accelerate the improvement of models” and enhance evaluation capabilities of the MDTF software package. This proposed study addresses the MAPP’s goals to “use observations to develop direct and indirect constraints on models’ climate sensitivity and apply them to reduce model projections’ uncertainty focusing on temperature projections.”


Principal Investigator (s): Hui Su

Co-PI (s):Jonathan H. Jiang, Kevin Bowman, Leo J. Donner,

Task Force: Climate Sensitivity Task Force

Year Initially Funded:2020

Competition: Constraining Models’ Climate Sensitivity

Final Report:

Investigating SST Pattern Controls on Cloud Feedbacks in CMIP6 Coupled Models

For over three decades, cloud feedback has remained the major source of uncertainty in model predictions of climate sensitivity. While progress has been made in identifying processes the regulate cloud feedback, the intermodel spread in cloud feedback has not been reduced. However, this interpretation is complicated by the fact that the various coupled ocean-atmosphere models used to make these projections have two types of emergent differences in their SSTs: First, they start out with widely varying base states due to different climatological SST biases; Second, their projections of SST changes and how they map onto the regional biases in SST also differ between models. Although these emergent large-scale SST differences ultimately arise from differences in model formulation, we hypothesize that the consequence of inter-model SST differences have impacts that are independent from the model formulation. The objective of this research is to better understand how biases in the base state and inter-model differences in the regional patterns of SST warming both influence and are influenced by cloud feedback.

We propose to better understand the contributions of regional biases in both climatological SST
and patterns of SST change from CMIP6 on their projections of cloud feedback through an analysis of CMIP6 and CFMIP.v3 simulations, and a series of coordinated modeling experiments designed to fill gaps in the CFMIP.v3 framework.

The initial analysis will exploit the suite of CFMIP.v3 atmosphere-only simulations to better
understand how the pattern of SST change impacts cloud feedback in CMIP6 models. The second
phase of this work will then perform a series of experiments using a GFDL atmospheric GCM
with climatological SSTs from other CMIP6 coupled models to investigate how biases in the mean
state of each of the CMIP6 models impact the cloud feedback in the GFDL model. These
experiments will be done with two different versions of the GFDL GCM that have both low and
high sensitivity (GFDL AM2, AM4).

The final set of experiments will use surface flux adjustments to artificially force the model’s
climatological SST to that of an exogenous model from CMIP6 (the “target” model). The GFDL
coupled model will then be integrated under both control and 4xCO2 conditions to determine how
the pattern of SST change is modulated by the climatological bias of that particular “target” model.
Our hypothesis is that climatological SST biases contribute to inter-model differences in the
pattern of SST change and that these, in turn, influence the cloud feedback. Our prediction is that
the cloud response and feedbacks of the GFDL model will move towards those of the “target”
model simply due to the artificial adjustment of the climatological SSTs.

Through these sets of experiments, we aim to both quantify and better understand the role of
climatological SST biases and patterns of SST response on the cloud feedbacks in CMIP6 models.
This will directly support the MAPP program goal to “develop new/improved methodologies and
integrate new understanding and data to improve our understanding and assessment of climate
sensitivity, explaining differences in sensitivities among models and generations of models.”


Principal Investigator (s): Gabriel A. Vecchi

Co-PI (s):Brian Soden

Task Force: Climate Sensitivity Task Force

Year Initially Funded:2020

Competition: Constraining Models’ Climate Sensitivity

Final Report:


Developing better constraints on Earth’s Equilibrium Climate Sensitivity (ECS) is one of the
central goals of climate science, but despite decades of work, there is still large uncertainty in
Earth’s ECS. The response of low clouds to warming has been identified as the primary source of
this uncertainty and, while recent evidence suggests that low cloud cover is reduced in warmer
climates, uncertainty around the sign and, especially, the magnitude of the low cloud response is
responsible for much of the spread in climate models’ ECS.

To further refine our understanding of the link between low clouds and ECS, and to develop
potential constraints on the behavior of low clouds with warming, we propose studying the
unforced variability of low-level clouds and their governing meteorological conditions over the
global ocean in order to identify specific geographic regions (“hot-spots”) in which the
variability of low cloudiness is especially indicative of models’ response to warming. The
proposed project will combine data from simulations with comprehensive climate models,
including from CMIP6 and from perturbed physics ensembles with models developed at NOAA’s
Geophysical Fluid Dynamics Laboratory (GFDL), with observations to (1) evaluate model skill
in representing the meteorological conditions governing low cloudiness, (2) identify ‘hot-spot’
regions in which unforced low cloud variability is strongly related to the forced low cloud
response, and (3) combine metrics of model skill in simulating the governing meteorology and of
low cloud variability in the hot-spots with observations to develop emergent constraints on
Earth’s ECS. A number of steps will be taken to ensure that the emergent constraints are robust –
both physically and statistically – and not simply the result of data-mining. The proposed
analysis will consider variability on time-scales from monthly to the 2-5 year time-scales of the
El Nino Southern Oscillation, and will consider low cloud regions over the entire global ocean.
Comprehensively characterizing low cloud variability, and its relationship with the governing
meteorological conditions, in CMIP5/6 and GFDL models is an important additional benefit of
the proposed project.

This project fits squarely within the aims of the MAPP competition, and addresses Priority Areas
A, B and C, as we will quantify uncertainty associated with low clouds in CMIP6 models (and in
climate models developed at GFDL), develop new metrics for assessing climate models, and use
observations to assess the models and develop emergent constraints. Additionally, the project
will provide a more refined view of the causes of intermodel differences in ECS and a better
understanding of the relationships between model parameters and low cloud variability in the
GFDL models. More broadly, the project will help NOAA prepare for the potential impacts from
increased atmospheric CO2 concentrations by reducing uncertainty in Earth’s ECS and by
improving the models used by NOAA to forecast future climate states.


Principal Investigator (s): Nicholas Lutsko

Co-PI (s):Joel Norris, Ming Zhao, David Paynter

Task Force: Climate Sensitivity Task Force

Year Initially Funded:2020

Competition: Constraining Models’ Climate Sensitivity

Final Report:

Constraining and Understanding Climate Sensitivity with Process Oriented Diagnostics

The project is a holistic, bottom-up approach to understanding climate sensitivity focusing
on cloud processes. This is particularly important now because the new generation of models
participating in the Coupled Model Intercomparison Project (CMIP6) appear to show a larger
spread in estimates of climate sensitivity than expected, and in particular several prominent models
show high climate sensitivity. Among those models is the Community Earth System Model, version
2 (CESM2); this project will especially focus on understanding the high climate sensitivity in that
model. Initial work shows that there is a strong cloud response to increased carbon dioxide in
CESM2, providing a positive feedback that amplifies the warming. The proposed research delves
into the processes that drive that cloud feedback.

Using a large suite of existing simulations from the ongoing CMIP6 model intercomparison
projects, a detailed, process-oriented analysis of clouds in CESM2 will be conducted. Simulations
of the current climate will be evaluated using reanalysis and observations. A major emphasis will
be to utilize the Cloud-Feedback Model Intercomparison Project (CFMIP) simulations that include
output from the CFMIP Observational Simulator Package (COSP). This allows the simulation to be
“observed” as if by satellites, providing a consistent definition of clouds between satellite products
and model output.

Process-oriented diagnostics will be developed that focus on cloud macrophysical and micro-
physical processes. The macrophysical focus will emphasize the structure of the turbulent boundary
layer. A mixed-layer model will be used to diagnose key mixing processes at the top of the bound-
ary layer. A regime-based approach will separate cloud types, providing a fine-grained view of the
response of clouds to climate forcing. The microphysical focus will emphasize warm rain processes
and aerosol-cloud interactions, and how each change with climate forcing. The same regime-based
approach will be applied. The macrophysical and microphysical factors may work in concert or
oppose each other in different regimes and under different forcing, and the diagnostics developed
here will expose these balances.

The process-oriented diagnostics will be developed for CMIP-style model output. That will
allow them to be applied to the multi-model ensemble to better understand cloud processes and
the relationship to climate sensitivity. The comparisons with observations provide physically-based
constraints on the model processes; such constraints may be used make a more confident statement
about the range of plausible climate sensitivity in current models, and will also directly inform
model development activities. The diagnostics will be developed using the Model Diagnostics Task
Force (MDTF) framework, and they will be submitted to the Task Force for consideration to be
included in the package. Therefore this project directly contributes to NOAA’s long-term climate
research goals by elucidating the role of clouds in a changing climate, providing observation-based
constraints on the physical processes that contribute to uncertainty in climate projections, and
providing new information and tools that can be used by the broad scientific community and in
assessments of climate change such as the National Climate Assessment.



Principal Investigator (s): Brian Medeiros

Co-PI (s):Andrew Gettelman

Task Force: Climate Sensitivity Task Force

Year Initially Funded:2020

Competition: Constraining Models’ Climate Sensitivity

Final Report:

Evaluating and constraining models’ stratocumulus and cumulus cloud feedbacks in the tropics using satellite observations to reduce uncertainties in future climate projections

Global circulation models (GCMs) exhibit large variability in cloud
feedbacks, which limits our ability to narrow the uncertainty in models’ climate sensitivity. Part
of this uncertainty is connected to the parameterizations controlling two categories of low clouds:
stratocumulus (Sc) and shallow cumulus (Cu) clouds produced by turbulence and shallow
convective parametrizations, respectively. The two cloud types have different regional cloud
amount and feedbacks, although the mechanisms involved remain unclear. Constraining the
geographic distribution and sensitivity to environmental conditions for each of these low clouds
separately would ensure more realistic low-cloud feedbacks and help reduce the spread in model
equilibrium climate sensitivity (ECS). Such a constraint, or even a basic evaluation of the type-
resolved low clouds produced by GCMs, has been hampered a lack of both global-scale
observations that distinguish these cloud types and robust method to discriminate their regimes in
climate model.

In this project, we propose to use a new observational constraint derived from CloudSat-
Cloud-Aerosols Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations,
which discriminate Sc from Cu clouds, in concert with three different methods to separate Sc-
from Cu-dominated regimes, to:
• Evaluate the Sc and Cu cloud amount (geographical distributions and profiles) as simulated by
coupled model intercomparison project phase 5 (CMIP5) and phase 6 (CMIP6) climate models
as well as their associated radiative fluxes on a global scale.
• Characterize and evaluate the interannual variation of the Sc and Cu fraction (including the
vertical change) in response to surface temperature forcings and their feedbacks.
• Investigate the relationships between cloud-controlling factors and each Sc-Cu cloud type in
the observations and use it to not only evaluate how well they are replicated in CMIP5 and
CMIP6 models (informing us on how well cloud processes are represented in the PBL
parametrizations) but also to infer observationally-based future cloud feedbacks.
• Assess the impact of the Sc-Cu cloud partitioning and feedbacks on the ECS, analyze the
mechanisms behind it and estimate an observationally-constrained ECS.
• Determine what type of the parametrizations in the planetary boundary layer (turbulence and
convection) produce the most plausible Sc-Cu distribution, feedbacks and relationships with
cloud-controlling factors and for what reasons (to provide better guidance for future model

Relevance of the project. We will address priority A and B of the MAPP competition 5, called
“Constraining models’ climate sensitivity”, with a type I project by narrowing the uncertainty
in the contribution of low clouds to model climate sensitivity and proposing new process-level
evaluation of model parametrizations using satellite observations. In addition, we will also address
priority C by proposing a method to observationally constrain model ECS.
The two main outcomes of the project are to reduce the uncertainty in model ECS due to low-
cloud feedback contributions and to propose new guidance for model development in specific
parametrizations (turbulence and convection). These will clearly benefit the broader community
and will directly support NOAA’s long-term research goals “weather and climate research” and “Modeling”.

Award Announcement:

Principal Investigator (s): Gregory Cesana

Co-PI (s):Robert Pincus, Andrew Ackerman

Task Force: Climate Sensitivity Task Force

Year Initially Funded:2020

Competition: Constraining Models’ Climate Sensitivity

Final Report:

Process-level metrics for evaluating the realism of CMIP6 models’ climate sensitivity based on multiple lines of observational evidence

In this proposal for MAPP competition 5 (Constraining Models’ Climate Sensitivity) we will develop diagnostics of the radiative feedbacks in CMIP6 models contributing to equilibrium climate sensitivity (ECS). We will then assess the realism of these feedbacks against observational constraints using multiple lines of evidence. Our focus will be on previously-proposed metrics of ECS that permit like-with-like comparisons between models and observable quantities. Research has demonstrated that such like-with-like comparisons are essential for ensuring that equivalent physical processes are compared so that unbiased estimates of ECS are made. We will also evaluate the ability of the observable metrics to constrain ECS and future warming. The CMIP6 model ensemble offers a unique opportunity to perform an out-of-sample test of proposed ECS metrics by assessing their ability to predict ECS in a different model ensemble from the one in which they were identified (generally CMIP5). We will then devise approaches for constraining models’ ECS using multiple key observational metrics at once. The specific objectives of this proposal are fourfold:
(1) Identify the processes that have driven the increase in ECS in CMIP6 models relative to CMIP5 and the processes most responsible for uncertainty in ECS in both model ensembles.
(2) Assess a suite of observational metrics to determine which most strongly constrain ECS and 21st century warming within CMIP5/6 models, with a particular focus on observational metrics that permit like-with-like comparisons with models.
(3) Use observations of the key metrics we identify to evaluate the realism of CMIP5/6 climate
models’ ECS.
(4) Develop process-oriented analysis tools, designed to run on generic CMIP6 output formats, that permit a given models’ ECS to be benchmarked against multiple observational metrics to facilitate model improvement. We are enthusiastic about working with the NOAA Model Diagnostics Task Force (MDTF) to create new process-oriented analysis tools for evaluating the realism of climate models’ ECS using multiple lines of observational evidence. Our analysis scripts will be designed to run on generic CMIP6 output formats and will be implemented as process oriented diagnostic module within the MDTF framework. Our NOAA-GFDL collaborator Dr. Yi Ming, co-lead of the MDTF, will assist us with these efforts and with other components of the proposed research. We also plan to work with our NCAR collaborator Dr. Daniel Amrhein and with our LLNL collaborator Dr. Mark Zelinka on various aspects of the research, as outlined in the proposal.

Relevance to the NOAA MAPP Competition and NOAA’s long-term goal
Our proposed research aims to constrain climate models’ ECS using multiple lines of evidence that permit like-with-like comparisons between models and observations. By identifying the processes relevant to ECS in models and providing observational constraints on those metrics, this work will provide new benchmarks for model development and improve the accuracy with which future climate change can be predicted. This project is aligned with MAPP’s mission to enhance the Nation's capability to understand and predict natural variability and changes in Earth's climate system and with NOAA’s long-term goal of providing the essential and highest quality environmental information vital to our Nation’s safety, prosperity and resilience. We propose to develop systematic process-oriented analysis methods and scripts for community use within the MDTF framework that can be used by modeling centers to compare model diagnostics with observational constraints.


Principal Investigator (s): Kyle Armour

Co-PI (s):Aaron Donohoe, Cristian Proistosescu

Task Force: Climate Sensitivity Task Force

Year Initially Funded:2020

Competition: Constraining Models’ Climate Sensitivity

Final Report:

Understanding Bulk Surface Flux Algorithm Contributions to Climate Projection Uncertainties

Modern Earth System models (ESMs) utilize a variety of bulk surface flux algorithms to compute the transfer of heat, water, and momentum across the air-sea interface. When compared to direct covariance flux measurements from research ships, fluxes estimated from many bulk algorithms are biased by about 10%-20%, with the majority of algorithms overestimating the flux. Surface flux biases lead to erroneous exchanges of energy between the ocean and atmosphere. For the ocean, flux biases can contribute to biases in circulations and sea surface temperature (SST). In the atmosphere, flux biases can affect the frequency, intensity, and vertical structure of convection, its radiative feedbacks, and the large-scale circulation response to its heating. The surface flux bias of a given model will imprint onto its estimated equilibrium climate sensitivity (ECS) through the the response of ocean circulations and cloud properties to the flux bias. The COARE3.5 bulk flux algorithm is the most recent version of the COARE bulk flux algorithm, originally developed in 1992 as part of the TOGA COARE field campaign. Its predecessor, the COARE3.0 algorithm, has been shown to produce some of the smallest flux biases when compared to other bulk algorithms; it is the basis for nearly all modern satellite-derived surface flux products. In this study, we will investigate surface flux feedbacks in observations, and leverage the COARE3.5 bulk flux algorithm to assess biases in surface fluxes and their feedbacks for models participating in CMIP6. Our work plan will:
1. Demonstrate flux algorithm diversity in CMIP6 models through average fluxes
conditionally sampled by wind speed and near-surface vertical humidity gradients. Estimate
model-dependent surface biases attributable to bulk flux algorithm by comparison of fluxes
computed using model input variables to the COARE3.5 algorithm.
2. Characterize observed and simulated surface flux feedbacks to diabatic heating associated
with clouds as a function of tropical circulation and global cloud regimes, and estimate
“theoretical” feedbacks for model fluxes computed with the COARE3.5 algorithm.
3. Assess the impact of improved surface flux calculations on cloud distributions and radiative
effects, and ECS in the CESM2 by replacing the native flux algorithm with the COARE3.5
algorithm and repeating pre-industrial control and 4xCO2 simulations.
4. Contribute surface flux diagnostics to NOAA MAPP Process-Oriented Diagnostic library.

The proposed work is relevant to Competition 5 (MAPP: Constraining Models’ Climate
Sensitivity) because: 1) it targets improved understanding of the clouds and convection coupling
with the ocean surface through improved in surface flux estimates, and 2) it aims to reduce the
uncertainty of cloud radiative effects and future climate projections arising from biases in
simulated fluxes. Our proposal targets Priority Areas B (defining key process-level metrics and
diagnostics) and C (using observations to develop constraints on models’ climate sensitivity).
This research will advance core capabilities in Earth system science and modeling. It
addresses NOAA Climate Adaptation and Mitigation objectives for “Improved scientific
understanding of the changing climate system” and “Assessments of current and future states of
the climate system.”

Award Announcement:

Principal Investigator (s): Charlotte DeMott

Co-PI (s):Carol Anne Clayson

Task Force: Climate Sensitivity Task Force

Year Initially Funded:2020

Competition: Constraining Models’ Climate Sensitivity

Final Report:

Assessing the predictability and probability of 21st century rain-on-snow flood risk for the conterminous U.S.

The U.S. faces challenges and bears high risk related to flood prediction and the protection of
life, property and infrastructure. For much of the Nation, the timing of heavy rainfall can coincide with 
seasonal snow-cover. The combined rainfall and melt during so-called rain-on-
snow (ROS) events has historically contributed to some of the Nation’s most destructive and costly
floods. Decision-makers critically lack guidance on ROS flood risk. Assessment requires
accurate estimates of antecedent snowpack, rain-snow height levels, the energy exchange
between the atmosphere and snow surface, rainfall intensity, and soil ice and moisture content. In
a future climate, each of these variables – and the integrated response of ROS flood risk – is
expected to change in complex and often contradictory ways. Notably, will projected increases in
precipitation extremes and winter rainfall increase ROS occurrence and the associated flood risk?
Or will less snow-cover and larger soil moisture deficits reduce ROS flood risk in a warmer
climate? The projected changes are likely to vary by region, season, climate model, emissions
scenario, and future time horizon. We address this grand challenge in hydrology and climate

The goal of our project is to assess national-scale historical (20th century) and future (21st
century) projections of integrated ROS flood risk and the associated confidence / uncertainty as
represented in a suite of CMIP6 climate models.

To achieve this goal, we will use a computationally efficient atmospheric model to dynamically
downscale: 1) historical reanalysis, which provides a baseline against which to compare
projected changes, 2) CMIP6 GCM output (historical and future scenarios), which offers an
assessment of uncertainty due to model error, and 3) a large ensemble from a single GCM
(historical and future), which offers insight into the role of internal climate variability. While
historical and future ROS flood risk assessment is the primary goal of the proposed research, the
intermediary assessment of the climate sensitivity of a full suite of ROS-relevant metrics has
high value and interest spanning environmental disciplines and NOAA Line Offices. We will
address a destructive flood mechanism affecting much of the Nation that intrinsically includes
climate-sensitive snow water resources, soil moisture deficits, and rainfall intensity.
We directly address the NOAA MAPP Program mission to enhance the Nation's capability to
predict variability and change in Earth's climate system. We will compare, integrate, and analyze
weather and climate model output to improve scientific understanding of projected changes in a
costly and destructive flood generating mechanism that affects much of the US and the northern
hemisphere. We aim to reveal and represent uncertainties to establish a defensible range of
quantitative storylines of integrated climate change impacts on ROS flood risk. Our assessment
of current and future states of the climate and hydrologic system will serve to identify potential
impacts needed to inform science, service, stewardship, mitigation and adaptation.

Climate Risk Area: Water Resources

Principal Investigator (s): Keith Musselman (UC Boulder)

Co-PI (s):Ethan Gutmann (NCAR), Flavio Lehner (NCAR), Angeline Pendergrass (NCAR)

Task Force: CMIP6TF

Year Initially Funded:2019

Competition: 21st Century Integrated U.S. Climate Predictions & Projections

Final Report:

Understanding the Role of Radiative Forcing and Cloud-Circulation Feedbacks on Spatial Rainfall Shifts in CMIP6

A robust prediction of all climate models is for wet regions to become wetter and dry
regions to become drier in response to increased greenhouse gases. The physical
processes that drive this response arise from increased water vapor and are generally
considered to be well understood. In contrast, the processes that govern spatial shifts of
rain belts are less well understood, despite the fact that such changes also have profound
societal consequences. This is particularly relevant in the tropics and sub-tropics due to
their large spatial gradients between wet and dry regions.

Recent research has highlighted the importance of radiative forcing from both greenhouse
gases and aerosols in driving large-scale shifts in rainfall patterns through their influence
on the atmospheric circulation. The instantaneous radiative forcing from greenhouse
gases has been shown to drive large-scale changes in the monsoonal circulations.
Likewise, the strong hemispheric asymmetry in aerosol radiative forcing has been shown
to drive large-scale changes in the meridional circulation. Both of these radiatively-forced
circulation changes have direct impacts in modulating the regional distribution of rainfall.
Unfortunately, recent studies have highlighted significant biases in model calculations of
radiative forcing under identical emission scenarios. Such biases remain largely
undocumented since radiative forcing is rarely calculated or archived, despite its
fundamental role in determining the forced response to anthropogenic emissions.

Due to their strong influence on atmospheric heating rates, clouds play a key role in
regulating the large-scale circulation of the atmosphere and therefore the regional
distribution, frequency and intensity of rainfall. Recent studies suggest that regional
shifts in rainfall may also be amplified through circulation-driven cloud feedbacks that
respond to, and enhance, the radiatively-forced rainfall change. The selection of “Clouds,
Circulation, and Climate Sensitivity” as one of the WCRP Grand Challenges underscores
both the importance and current lack of understanding regarding these processes.

This proposal seeks to exploit model simulations from CMIP6 along with idealized
forcing scenarios from RFMIP and PDRMIP to better quantify and understand the role
of instantaneous radiative forcing and cloud-circulation feedbacks in modulating shifts in
the spatial distribution and intensity of rainfall.

The primary objectives of this proposal are to:
i) Develop and apply process-oriented metrics to quantify and evaluate model simulations
of instantaneous radiative forcing and cloud-circulation feedbacks;

ii) Quantify the impacts of radiatively-forced circulation changes on the regional
distribution of rainfall;

iii) Use historical observations in conjunction with spatial fingerprinting techniques to
better constrain the representation of these radiative and cloud processes in models.
In accomplishing these objectives, we will directly contribute to the NOAA MAPP goal
of developing and applying process-oriented metrics to better understand sources of
model bias involving “cloud and radiative processes” and their impact on “weather and
climate extremes”.

In accomplishing these objectives, we will directly contribute to the NOAA MAPP goal
of developing and applying process-oriented metrics to better understand sources of
model bias involving “cloud and radiative processes” and their impact on “weather and
climate extremes”.

Principal Investigator (s): Brian Soden (University of Miami)

Co-PI (s):

Task Force: Model Diagnostics Task Force

Year Initially Funded:2018

Competition: Addressing Key Issues in CMIP6-era Earth System Models

Final Report:

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