Funded Projects

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310605 | View Publications on Google Scholar

Extratropical cyclones, which make up the mid-latitude storm tracks, are the key driver producing cool season precipitation in the CONUS. Hence, biases and errors in cyclone and storm track prediction, or in the structure of cyclone related precipitation, can give rise to biases in model predicted precipitation. S2S prediction models can have biases either in the mean climate or in the prediction of climate variability. Hence, precipitation biases and errors over the CONUS can arise due to model biases and errors in: 1) the prediction of climate drivers such as ENSO, MJO, polar vortex and the QBO; 2) the mid-latitude teleconnection patterns associated with these climate modes; 3) the response of extratropical cyclones to these large scale teleconnections; and 4) the precipitation structure associated with extratropical cyclones. Preliminary results by the PIs’ groups have shown that S2S model simulations exhibit several of these biases. This project will diagnose extratropical cyclone related precipitation biases, including biases in extreme precipitation, using GFDL’s SPEAR model simulations. We will evaluate the model bias in winter cyclone frequency/intensity using reanalysis data and examine the linkage between cyclone frequency/intensity bias and precipitation bias at the S2S time scale. Precipitation biases will be assessed using rain gauge- and satellite-based precipitation estimates. Model biases will be stratified according to geographical regions, cyclone paths and evolution, as well as cyclone intensity. Biases in model cyclone and precipitation response to the modes of climate variability discussed above will be quantified. We will diagnose the cyclone related synoptic precipitation structural errors in model simulations using observations and identify the key processes causing these structural errors. Model sensitivity studies will be conducted to provide insights on how these biases may be reduced. Sensitivity to model resolution, sea surface temperature biases, biases in tropical forcing, biases in the large-scale circulation, and biases in model dynamics and physics will be assessed using model intervention experiments. The outcome from this work will inform GFDL’s model development team on how to improve cool season precipitation simulation and prediction. On top of that, our results will provide a detail account of the contribution of different kinds of extratropical cyclones to the mean and extreme precipitation over CONUS, as well as how well SPEAR simulates these contributions. Selected analyses will be conducted to examine biases exhibited by CFSv2 and GEFSv12 subseasonal predictions. This project aims to identify and understand model precipitation biases and systematic errors associated with extratropical cyclones and storm tracks, which are the key physical drivers for precipitation over the CONUS during the cool season, through data analysis and global modeling experiments. While the numerical experiments will be conducted using the GFDL SPEAR model, our diagnostic studies will also be conducted on CFSv2 and GEFS simulations to identify biases and systematic errors in these models. Diagnostic tools developed in this project can also be applied to diagnose model errors and biases in future model simulations and predictions, including those of the subseasonal UFS. Our results will provide insights to model developers on the sources of cool season precipitation biases, informing future model development, thus this project is clearly relevant to the competition and to NOAA’s mission.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310606 | View Publications on Google Scholar

Precipitation processes are multi-scale in nature. A faithful representation of precipitation in a model relies on its capability to capture 1) large-scale atmospheric circulation patterns that trigger the development of a precipitating weather event such as cyclones and thunderstorms, and 2) local, smaller scale physical processes (including convection, radiation, cloud physics, air-sea interaction, etc.) that determine the lifecycle of a weather event through their interactions with large-scale flow. In the central United States, warm season (March-August) precipitation is mainly associated with Mesoscale Convective Systems (MCS), a form of “layered” overturning circulations that is often poorly resolved or parameterized in a global climate model. The failure of such parameterizations to realistically account for scale-interactions, together with model intrinsic biases in reproducing large-scale forcing of MCSs, poses a major challenge in our effort to simulate and predict warm season precipitation, particularly across the S2S timescales. In response to this challenge, here we propose a multi-scale diagnostics hierarchy for detecting, source-tracking, understanding and reducing model biases in the US warm season S2S precipitation variability. The cornerstone of this hierarchy is the partitioning of MCS processes into two components: large-scale forcing and local, smaller scale physics. Teasing out large-scale forcing from a myriad of interacting scales of an MCS allows one to potentially trace the origin of MCS variability and identify remote sources of predictability for MCS precipitation. By integrating data diagnosis with numerical modeling, the PIs will develop the diagnostics hierarchy targeting processes of MCS initiation, growth and decay. Specific tasks to be carried out include 1) constructing new evaluation metrics to quantify the S2S variability in the U.S. warm season precipitation, 2) statistical mapping of MCS variability onto S2S precipitation variability, 3) partitioning the GFDL AM4’s MCS biases into components associated with large-scale forcing and model physics, 4) multi-scale diagnostics and idealized modeling to reveal the dynamical nature of model biases in MCS large-scale forcing, 5) experimenting with new packages of model physics to further understand the contribution of local processes to MCS biases, and 6) connecting model biases in MCS large-scale forcing with modes of climate variability and exploring remote sources of S2S predictability for MCSs with NOAA-funded field campaign observations. The proposed project is a direct response to the joint competition to “advance process understanding and representation of precipitation in models”. Aiming at the longstanding problem of MCS simulation, we will develop, test, and deliver to the community an innovative multiscale diagnostic framework that encompasses process-level metrics development, scale-resolving diagnostics, error partitioning, source tracking, and generation of dynamics-based guidance for model optimization and update. This work contributes directly to the goal of “Focus Area A: Identifying and understanding key processes that influence model biases and systematic errors in the simulation of precipitation at the subseasonal to seasonal (S2S) timescale”. The insights gained from the scale-resolving bias attribution will also pave the way for formulating and testing (with NOAA field campaign observations) hypotheses regarding remote sources of S2S predictability of precipitation from the tropical Indo-Pacific and Atlantic. Given the significance of S2S precipitation forecasts for hazards mitigation and water resource management, the proposed project will ultimately contribute to the objective of the NOAA CPO - “advancing scientific understanding, monitoring, and prediction of climate and its impacts to enable effective decisions”.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310607 | View Publications on Google Scholar

El Niño forecasts in seasonal forecast models struggle with El Niño false alarms and over-confident forecasts under conditions of recent westerly wind bursts or an extended western Pacific warm pool. There are two major contributors to El Niño prediction: ocean subsurface heat content and westerly wind bursts. Westerly wind bursts, which can more generally be described as weather events, play a major role in the spread of the El Niño forecasts. They interact with the upper ocean at the edge of the warm pool to extend the warm pool eastward through momentum, heat, and freshwater fluxes. We hypothesize that forecast models struggle to correctly capture the interaction between weather events and the warm pool edge, which leads to over-confident El Niño forecasts and false alarms. Specifically, we hypothesize that forecast models do not correctly represent the upper ocean response to the strong precipitation associated with weather events at the warm pool edge. We therefore propose to study the role of weather events in producing over-confident El Niño forecasts. We will use forecasts from the North American Multi-Model Ensemble (NMME) and Subseasonal Experiment (SubX) projects to assess the role of weather events in producing El Niño, comparing to ocean reanalysis and satellite data. Details of the weather events will be quantified through mixed-layer heat and salt budgets computed from reanalysis data; comparison to forecast data will enable us to identify the processes that forecast models may be getting wrong. A major focus will be the role of precipitation, which generates upper ocean stratification anomalies that may not be well captured by models. We will assess the impact of both the precipitation fields in the forecast models as well as their representation of upper ocean processes in forecast skill. We will also look at how the ocean response influences the creation of future weather events.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310608 NA22OAR4310609 NA22OAR4310610 | View Publications on Google Scholar

Tropical cyclones (TCs) are a major source of extreme precipitation in tropical and subtropical regions. Accurate TC forecasts are key to predicting precipitation at the subseasonal time scale in the CONUS as landfalling TCs often bring extreme rain, especially to the coastal regions. Given the potentially catastrophic societal impacts of torrential TC precipitation and their potentially negative influence on a model’s subseasonal precipitation prediction skill, if not simulated correctly, there is a clear need to evaluate subseasonal TC prediction and understand its skill in models. We propose a project focused on the subseasonal prediction of TCs and their associated precipitation in the CONUS in the Unified Forecast System (UFS) and other models. We aim to identify and understand model biases and systematic errors in the representation of the Madden-Julian Oscillation (MJO)-TC relationship, a key source of predictability for subseasonal TC prediction. Under the proposed research, we will first conduct performance-based analyses to objectively evaluate the performance of UFS and other models at predicting the MJO and its circulation anomalies during boreal summer, as well as the modulation of TC precursor disturbances and TC activity at subseasonal timescales in the Northeast Pacific and North Atlantic basins. We will then perform process-based analyses targeting the dynamics and thermodynamics of precursor disturbances and their conversion into TCs (i.e., tropical cyclogenesis) and precipitation associated with TCs and TC remnants in the CONUS coastal and inland regions, which will provide insights into the origins of precipitation forecast error. This will include a diagnosis of errors in the subseasonal modulation of TC precursors and TCs, even if a model is able to produce good MJO predictions.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310611 | View Publications on Google Scholar

Seasonal to subseasonal (S2S) variability in tropical precipitation and mid-latitude weather is largely controlled by the evolution of tropical precipitation phenomenon. At subseasonal timescales, the Madden-Julian Oscillation (MJO) dominates variability in precipitation. Cloud systems in the MJO release latent heat, which produces a Rossby wave response that propagates in wave trains away from the equator and impacts extreme weather events through Earth’s middle latitudes. However, numerical models of the atmosphere, including NOAA’s Global Forecast System and Climate Forecast System, struggle to demonstrate skill in tropical rainfall beyond about 21 days even though observational analysis strongly suggests that the MJO is related to extreme weather events in the United States at much longer lead times. One particular difficulty in modeling the MJO is what is known as the “barrier effect”, which is the tendency for the convective envelope to stall over the Maritime Continent without reaching the Western Pacific. Numerical models of the global atmosphere dissipate the MJO convection more frequently over the Maritime Continent than is indicated in observations. As a result, most general circulation models are biased in that the mid-latitude weather events that are connected to MJO activity in the Western Pacific are not well predicted. Guided by theoretical descriptions of MJO as a moist equatorial wave, we will use a combination of observations and numerical models to evaluate hypotheses related to the physical processes that control maintenance or decay of the MJO-related moisture anomaly over the Maritime Continent. Our work will incorporate physical parameterizations used in NOAA numerical models with the intention that NOAA’s operational forecast suite can take advantage of our findings more quickly than if other non-NOAA modeling frameworks were used. As the global climate continues to warm during upcoming decades, improving short- to medium-range predictability of extreme weather events linked to S2S tropical variability will be essential to mitigating the damage and loss of property and human lives in the United States and throughout the world. Improving our understanding of S2S variability—and specifically the MJO—in the current climate will allow the scientific community to more accurately assess the impacts of the MJO on extreme events that impact the United States in a warmer climate. Our research will contribute to the CPO’s mission of increasing resilience of the United States and its strategic geopolitical partners as society encounters uncertain future climate-related challenges. Specifically, our research will explore how unrealistic assumptions made in some of NOAA’s physical parameterizations of the atmosphere may negatively impact the numerical representation of processes that are important for short- and medium range predictability of tropical precipitation and related mid-latitude weather phenomena in both the current and in future, warmer climates.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310612 | View Publications on Google Scholar

Despite its significant role in the global climate, the Maritime Continent (MC) remains somewhat of an enigma due to a lack of physical understanding and model performance. The MC connects the Indian and Pacific Oceans, and tropics and extratropics through teleconnections. It also acts as a predictability barrier for subseasonal-to-seasonal (S2S) prediction of precipitation that depends on many interacting processes including the land-atmosphere interactions, which are controlled through soil moisture, topography, and diurnal cycle of insolation, among others. Many of these processes, however, are poorly understood and inadequately represented in weather and climate models. In this work, we propose: (i) to describe the observed relationship between the S2S precipitation and other parameters such as soil moisture, topography, and diurnal cycle of insolation; (ii) to explore the relative contribution of soil moisture, topography, and diurnal cycle of insolation; (iii) to investigate the role of nonlinear interactions between soil moisture and insolation, and between topography and insolation on the S2S precipitation using a novel factor separation method from set theory; and (iv) to find how much bias in simulated S2S precipitation comes from the bias in the diurnal cycle of precipitation in the MC. To achieve these objectives, we will leverage a combination of NOAA and other observations, cutting-edge reanalysis products, a series of high-resolution cloud-permitting simulations using a limited-area model, and relatively coarse-resolution simulations using general circulation models. We will also use process-oriented model diagnostics (that were developed as part of prior NOAA grants) in combination with budget analyses of moisture and moist static energy to achieve our overarching goal of understanding the physical processes that modulate S2S precipitation in the MC.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310613 NA22OAR4310614 | View Publications on Google Scholar

The main objectives of the proposed project are to understand key processes of the diurnal cycle of rainfall over the islands of the Maritime Continent and to assess the relationship between subseasonal to seasonal (S2S) models’ representation of the diurnal cycle and their forecast skill of tropical precipitation. Convective activity over the Maritime Continent influences a wide range of global weather and climate phenomena. One of the key phenomena influencing convective activity of the region on S2S timescales is the Madden-Julian Oscillation (MJO). However, current global models exhibit significant biases when predicting the evolution of the MJO over the Maritime Continent, which are argued to stem from inaccurate representation of the diurnal cycle and its intraseasonal variability. The diurnal cycle explains a substantial fraction of precipitation variability over the Maritime Continent, and hence it is crucial to address model deficiencies related to the diurnal cycle. Therefore, it is essential to first understand how errors in the representation of the diurnal cycle impact subsequent S2S skill in operational models. To advance S2S predictions, we then need to identify the sources of error to accurately represent convective processes and simulate diurnal rainfall over the Maritime Continent. The proposed study will address the objectives through three major tasks. The first task will evaluate the model representation of the diurnal cycle over the Maritime Continent using S2S operational forecast datasets. We will identify relationships between short-range diurnal cycle errors and subsequent MJO prediction. This task will also quantify how the erroneous diurnal cycle relates to large-scale environmental variables, which will help us understand how the erroneous diurnal cycle impacts S2S predictions. After identifying how and when S2S models struggle to predict the diurnal cycle in the first task, the second task will use observations and process-oriented experiments using a cloud-resolving model during the Years of Maritime Continent (YMC) to understand key processes of the diurnal cycle. The second task will focus on identifying key processes for the development and offshore propagation of diurnal rainfall over the islands that global models often struggle to represent. To then understand the two-way feedback of the diurnal cycle and the MJO, the third task will examine the sensitivity of the diurnal processes to large-scale conditions using observations and process-oriented experiments using a regional cloud-permitting model.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310615 NA22OAR4310616 NA22OAR4310617 | View Publications on Google Scholar

Climate models of standard resolution (e.g. approximately 1°) do not properly resolve phenomena such as atmospheric fronts or ocean mesoscale features. Much of the precipitation in mid-latitudes is associated with atmosphere fronts, particularly in the Extratropical storm tracks. In addition, ocean mesoscale features such as western boundary currents and eddies can induce convergence of the near-surface winds, which is an important factor governing precipitation. Thus, the standard resolution models may be missing some key aspects of processes that drive precipitation, with detrimental impacts on longer range predictability and S2S associated with evolution of the ocean mesoscale and fronts. This project will aim to understand linkages between sea surface temperature (SST), surface convergence and precipitation, using high resolution datasets, and use the results to assess standard resolution model results. Key aspects to this work are how the ocean mesoscale SST affects the atmospheric boundary layer and frontogenesis. The proposed work falls into four steps: 1. Describe the co-variability of sea surface temperature, wind convergence and precipitation as a function of time and spatial scales from days to season, and from tens of kilometers to basin scale. 2. Investigate the atmospheric boundary layer responses to the ocean mesoscale in the presence of large-scale atmospheric forcing using a recently developed boundary layer model. 3. Characterize surface wind convergence and precipitation associated with atmospheric fronts using objective atmospheric frontal diagnostics, and explore linkages to the ocean mesoscale. 4. Explore how the responses of atmospheric boundary processes, atmospheric fronts, and mesoscale SST features, are related to each other. The state-of-the-art, high-resolution observational and model datasets to be used here include ERA5, CFSR reanalysis, high-resolution CESM, HighResMIP climate models, precipitation analyzed from satellite and in-situ data (IMERG) and scatterometer winds, and a recently developed boundary layer model (Schneider and Qiu 2015).

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310621 | View Publications on Google Scholar

Due to the coupled nature of the earth system, precipitation forecast errors at subseasonal-to-seasonal (S2S; 2 weeks to 2 months) lead times are caused by a combination of errors/biases from the atmosphere, ocean, ice and land across a range of spatial and temporal scales. Unrealistic sensitivity of model convection to its large-scale environment, as well as unrealistic strength of prominent feedbacks (e.g. cloud radiative, wind-evaporation), can lead to the inability to maintain subseasonal tropical convection variability in forecasts. Even if models were able to perfectly simulate tropical fields, errors in the subtropical and midlatitude circulations can further introduce forecasts errors in U.S. precipitation via incorrect teleconnections. This means that identifying the correct combination of model biases that lead to specific precipitation forecast errors is incredibly challenging. Furthermore, these different processes are not always active at any given time (e.g. the Madden-Julian oscillation can be in an inactive state), implying that their associated biases only contribute to forecast errors intermittently. Thus, an additional challenge in understanding model forecast errors at S2S lead times is identifying the intermittent states of the system when these biases are most important. Here, we propose to couple novel explainable artificial intelligence (XAI) techniques with process-oriented diagnostics to identify, understand, and correct via post-processing, state-dependent UFS precipitation forecast errors. The proposed work is organized into three distinct activities that focus on improving UFS forecasts of North American precipitation at S2S lead times. Activity I involves refining and then implementing an XAI framework to identify state-dependent UFS precipitation errors. Activity II revolves around understanding the tropical-extratropical teleconnection processes relevant to the climate states identified by the XAI method. To do this, we will use process-oriented model diagnostics and test our understanding with simplified model experiments. Activity III aims to leverage what we have learned in Activities I and II to develop XAI-derived post-processing corrections to improve UFS precipitation forecasts for specific initialization states.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310603 NA22OAR4310604 | View Publications on Google Scholar

Integration of the NOAA Unified Forecast System (UFS) Seasonal Forecast System (SFS) into the seasonal research and forecasting communities, including University of Miami and the Climate Prediction Center (CPC) relies on assessment of skill and biases of precipitation over North America in both hindcasts and realtime forecasts. The National Center for Atmospheric Research (NCAR)’s enhanced Model Evaluation Tools (METplus) verification framework is intended to be used to verify the UFS, and is currently being onboarded for operational use at CPC due to its large library of verification metrics and community support approach. A currently funded collaborative effort between NCAR and CPC shows that METplus requires more development to seamlessly integrate with seasonal climate data, such as UFS-SFS and the North American Multi- Model Ensemble (NMME) (part a). Moreover, CPC seasonal forecasters rely on the state of primary climate drivers to forecast seasonal precipitation, and information on these drivers is imperative to the seasonal climate research and modeling communities. Thus, the assessment of the impact of El Niño Southern Oscillation (ENSO), decadal trends, etc. on North American precipitation variability is key to diagnosing the utility of any dynamical models used in seasonal forecasting and research. Though ENSO plays a key role in precipitation variability, other climate drivers should also be considered. For example, key internal forcing mechanisms such as the Pacific Decadal Oscillation (PDO) and its impact on North American precipitation in seasonal forecast systems must be assessed, as well as the representation of in-situ drivers such as soil moisture and snow cover (part b). Collaboratively, we will create a verification framework utilizing METplus to allow streamlined assessment of probabilistic seasonal precipitation forecast skill, including hindcast and conditional skill related to the above key drivers within the UFS-SFS. An additional goal will be that it can be easily expanded to any climate model ensemble. The development, documentation, and demonstration of these process-based model capabilities will provide valuable feedback to the UFS model development team and community, with the potential to improve the key modes of variability that impact seasonal precipitation forecasts.