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Co-Evolving Climate Models under Uncertainty to Improve Predictive Skill
Focal Area: Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models Science Challenge: Advances in modeling of climate and improved observational capabilities have led to great improvements in understanding large-scale historical climate effects. It however remains a challenge to reliably predict the risk of fine-scale regional climate events on human systems even as such risks are on the rise because warmer and wetter climates are more prone to hydrometeorological extremes,
Land Boundary Conditions for the Goddard Earth Observing System Model Version 5 (GEOS-5) Climate Modeling System: Recent Updates and Data File Descriptions
The Earths land surface boundary conditions in the Goddard Earth Observing System version 5 (GEOS-5) modeling system were updated using recent high spatial and temporal resolution global data products. The updates include: (i) construction of a global 10-arcsec land-ocean lakes-ice mask; (ii) incorporation of a 10-arcsec Globcover 2009 land cover dataset; (iii) implementation of Level 12 Pfafstetter hydrologic catchments; (iv) use of hybridized SRTM global topography data; (v) construction of the HWSDv1.21-STATSGO2 merged global 30 arc second soil mineral and carbon data in conjunction with a highly-refined soil classification system; (vi) production of diffuse visible and near-infrared 8-day MODIS albedo climatologies at 30-arcsec from the period 2001-2011; and (vii) production of the GEOLAND2 and MODIS merged 8-day LAI climatology at 30-arcsec for GEOS-5. The global data sets were preprocessed and used to construct global raster data files for the software (mkCatchParam) that computes parameters on catchment-tiles for various atmospheric grids. The updates also include a few bug fixes in mkCatchParam, as well as changes (improvements in algorithms, etc.) to mkCatchParam that allow it to produce tile-space parameters efficiently for high resolution AGCM grids. The update process also includes the construction of data files describing the vegetation type fractions, soil background albedo, nitrogen deposition and mean annual 2m air temperature to be used with the future Catchment CN model and the global stream channel network to be used with the future global runoff routing model. This report provides detailed descriptions of the data production process and data file format of each updated data set.
Overview of Large-scale Tropospheric Transport in the Chemistry Climate Model Initiative (CCMI) Simulations
The transport of chemicals is a major uncertainty in the modeling of tropospheric composition. Here we compare the large-scale tropospheric transport properties among different models in the Chemistry Climate Modeling Initiative (CCMI) with a focus on transport defined with respect to the Northern Hemisphere (NH) midlatitude surface. Among simulations of the recent past (1980-2010) we show that there are substantial differences in their global-scale tropospheric transport properties. For example, the mean transit time since southern hemisphere air last contacted the NH midlatitude surface differs by ~30-40% among simulations. We show that these differences are most likely associated with differences in parameterized convection over the oceans, such that the spread in transport among simulations constrained with analysis fields is as large as the spread among free-running simulations.
Climate Models, Oracles and Predicting Future Climate
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Representation of Clear and Cloudy Boundary Layers in Climate Models
The atmospheric general circulation models which are being used as components of climate models rely on their boundary layer parameterizations to produce realistic simulations of the surface turbulent fluxes of sensible heat. moisture. and momentum: of the boundary-layer depth over which these fluxes converge: of boundary layer cloudiness: and of the interactions of the boundary layer with the deep convective clouds that grow upwards from it. Two current atmospheric general circulation models are used as examples to show how these requirements are being addressed: these are version 3 of the Community Climate Model. which has been developed at the U.S. National Center for Atmospheric Research. and the Colorado State University atmospheric general circulation model. The formulations and results of both models are discussed. Finally, areas for future research are suggested.
High-resolution climate model datasets for energy infrastructure planning in a renewable-dependent future
Electrification and renewables deployment efforts are amplifying the interdependence of the climate and energy systems. Increases in climate model resolution, which is now approaching that of reanalysis datasets and operational weather forecast models, present a unique opportunity to use future climate projections for energy infrastructure planning. In this Perspective, we review recent developments in high-resolution climate modeling, which have been driven by increased computing power and advanced software tools. We then look ahead to discuss how high-resolution climate data can be used to plan for a renewable-dependent future, and envision a unified climate-energy model framework that captures the two-way feedbacks between these interdependent systems.
Improving the Representation of Land in Climate Models by Application of EOS Observations
The PI's IDS current and previous investigation has focused on the applications of the land data toward the improvement of climate models. The previous IDS research identified the key factors limiting the accuracy of climate models to be the representation of albedos, land cover, fraction of landscape covered by vegetation, roughness lengths, surface skin temperature and canopy properties such as leaf area index (LAI) and average stomatal conductance. Therefore, we assembled a team uniquely situated to focus on these key variables and incorporate the remotely sensed measures of these variables into the next generation of climate models.
Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region
As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.
Hydrostatic and Non‐Hydrostatic Baroclinic Instability in the Dynamical Core of the DOE Global Climate Model
Abstract The dynamical core of the Department of Energy global climate model is used to understand the role of non‐hydrostatic dynamics in the simulation of dry and moist baroclinic waves. To reduce computational cost, the Diabatic Acceleration and REscaling approach is adopted. Scale analysis and numerical simulations suggest that the model solution is not distorted by the change of spherical metric terms due to the change of Earth radius, neither are the inter‐scale interactions strongly altered as indicated by the analysis of spectral flux. Compared with hydrostatic simulations, the onset of baroclinic instability is delayed under non‐hydrostatic dynamics as the associated weaker vertical motions tend to increase the critical wavelength and narrow the range of waves that can be baroclinically unstable. During the development of baroclinic waves, non‐hydrostatic dynamics tends to induce vertical motions in the upper troposphere, accelerating the eastward propagation of upper‐level ridges through their impact on local vorticity tendency. These processes reduce the westward tilt of the vertical ridge axes and suppress the conversion of mean flow available potential energy to eddy kinetic energy, leading to weaker baroclinic eddies than in hydrostatic simulations. The contrasts between hydrostatic and non‐hydrostatic settings hold in supplemental experiments that vary the background flow Rossby number, the amount of water vapor content and vertical resolution. We also find that mesoscale and smaller‐scale activities can be considerably under‐represented when the vertical resolution is limited to that typically used in global climate models.
Steady-state solutions of a diffusive energy-balance climate model and their stability
A diffusive energy-balance climate model, governed by a nonlinear parabolic partial differential equation, was studied. Three positive steady-state solutions of this equation are found; they correspond to three possible climates of our planet: an interglacial (nearly identical to the present climate), a glacial, and a completely ice-covered earth. Models similar to the main one are considered, and the number of their steady states was determined. All the models have albedo continuously varying with latitude and temperature, and entirely diffusive horizontal heat transfer. The stability under small perturbations of the main model's climates was investigated. A stability criterion is derived, and its application shows that the present climate and the deep freeze are stable, whereas the model's glacial is unstable. The dependence was examined of the number of steady states and of their stability on the average solar radiation.
Understanding Differences in Chemistry Climate Model Projections of Stratospheric Ozone
Chemistry climate models (CCMs) are used to project future evolution of stratospheric ozone as concentrations of ozone-depleting substances (ODSs) decrease and greenhouse gases increase, cooling the stratosphere. CCM projections exhibit not only many common features but also a broad range of values for quantities such as year of ozone return to 1980 and global ozone level at the end of the 21st century. Multiple linear regression is applied to each of 14 CCMs to separate ozone response to ODS concentration change from that due to climate change. We show that the sensitivity of lower stratospheric ozone to chlorine change Delta Ozone/Delta inorganic chlorine is a near-linear function of partitioning of total inorganic chlorine into its reservoirs; both inorganic chlorine and its partitioning are largely controlled by lower stratospheric transport. CCMs with best performance on transport diagnostics agree with observations for chlorine reservoirs and produce similar ozone responses to chlorine change. After 2035, differences in Delta Ozone/Delta inorganic chlorine contribute little to the spread in CCM projections as the anthropogenic contribution to inorganic chlorine becomes unimportant. Differences among upper stratospheric ozone increases due to temperature decreases are explained by differences in ozone sensitivity to temperature change Delta Ozone/Delta T due to different contributions from various ozone loss processes, each with its own temperature dependence. Ozone decrease in the tropical lower stratosphere caused by a projected speedup in the Brewer-Dobson circulation may or may not be balanced by ozone increases in the middle- and high-latitude lower stratosphere and upper troposphere. This balance, or lack thereof, contributes most to the spread in late 21st century projections.
Multi-model Mean Nitrogen and Sulfur Deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Evaluation Historical and Projected Changes
We present multi-model global datasets of nitrogen and sulfate deposition covering time periods from 1850 to 2100, calculated within the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The computed deposition fluxes are compared to surface wet deposition and ice-core measurements. We use a new dataset of wet deposition for 2000-2002 based on critical assessment of the quality of existing regional network data. We show that for present-day (year 2000 ACCMIP time-slice), the ACCMIP results perform similarly to previously published multi-model assessments. For this time slice, we find a multi-model mean deposition of 50 Tg(N) yr1 from nitrogen oxide emissions, 60 Tg(N) yr1 from ammonia emissions, and 83 Tg(S) yr1 from sulfur emissions. The analysis of changes between 1980 and 2000 indicates significant differences between model and measurements over the United States but less so over Europe. This difference points towards misrepresentation of 1980 NH3 emissions over North America. Based on ice-core records, the 1850 deposition fluxes agree well with Greenland ice cores but the change between 1850 and 2000 seems to be overestimated in the Northern Hemisphere for both nitrogen and sulfur species. Using the Representative Concentration Pathways to define the projected climate and atmospheric chemistry related emissions and concentrations, we find large regional nitrogen deposition increases in 2100 in Latin America, Africa and parts of Asia under some of the scenarios considered. Increases in South Asia are especially large, and are seen in all scenarios, with 2100 values more than double 2000 in some scenarios and reaching 1300 mg(N) m2 yr1 averaged over regional to continental scale regions in RCP 2.6 and 8.5, 3050 larger than the values in any region currently (2000). The new ACCMIP deposition dataset provides novel, consistent and evaluated global gridded deposition fields for use in a wide range of climate and ecological studies.
Multi-Model Mean Nitrogen and Sulfur Deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Evaluation of Historical and Projected Future Changes
We present multi-model global datasets of nitrogen and sulfate deposition covering time periods from 1850 to 2100, calculated within the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The computed deposition fluxes are compared to surface wet deposition and ice core measurements.We use a new dataset of wet deposition for 2000-2002 based on critical assessment of the quality of existing regional network data. We show that for present day (year 2000 ACCMIP time slice), the ACCMIP results perform similarly to previously published multi-model assessments. For this time slice, we find a multimodel mean deposition of approximately 50 Tg(N) yr−1 from nitrogen oxide emissions, 60 Tg(N) yr−1 from ammonia emissions, and 83 Tg(S) yr−1 from sulfur emissions. The analysis of changes between 1980 and 2000 indicates significant differences between model and measurements over the United States but less so over Europe. This difference points towards a potential misrepresentation of 1980 NH3 emissions over North America. Based on ice core records, the 1850 deposition fluxes agree well with Greenland ice cores, but the change between 1850 and 2000 seems to be overestimated in the Northern Hemisphere for both nitrogen and sulfur species. Using the Representative Concentration Pathways (RCPs) to define the projected climate and atmospheric chemistry related emissions and concentrations, we find large regional nitrogen deposition increases in 2100 in Latin America, Africa and parts of Asia under some of the scenarios considered. Increases in South Asia are especially large, and are seen in all scenarios, with 2100 values more than double their 2000 counterpart in some scenarios and reaching >1300 mg(N)m−2 yr−1 averaged over regional to continental-scale regions in RCP 2.6 and 8.5, 30-50% larger than the values in any region currently (circa 2000). However, sulfur deposition rates in 2100 are in all regions lower than in 2000 in all the RCPs. The new ACCMIP multimodel deposition dataset provides state-of-the-science, consistent and evaluated time slice (spanning 1850-2100) global gridded deposition fields for use in a wide range of climate and ecological studies.
Final Report on "Collaborative Research to Narrow Uncertainties in Precipitation and the Hydrological Cycle in Climate Models"
The central objectives of this work have been to explore and improve the depiction of precipitation and related processes in climate models by developing and analyzing observations and their gridded analyses, and developing new approaches and metrics. All three groups worked with the NCAR-based Community Earth System Model (CESM) and international community Climate Model Inter-comparison Project version 5 (CMIP5) models in various ways. At NCAR and SUNYA an initial focus was on the observational record, including updating of the river discharge dataset. A major focus at NCAR was extensive work on new datasets of hourly precipitation from CMORPH (observations) and CESM, and commentary on and validation of the CESM. SUNYA contributed to the comparison and evaluation of existing precipitation products widely used in climate research, the analysis of precipitation characteristics in CESM and other CMIP5 models, and decadal precipitation variability associated with decadal variability. At NCAR and SUNYA work was focused on the frequency and intensity of precipitation, including their dependence on the temporal and spatial resolution of the data used to calculate the frequency and intensity, as well as additional analyses on the effects of precipitation data uncertainties on the estimated historical drying trends. LLNL focused on the diurnal cycle. New insights have resulted in the characteristics of precipitation (frequency, intensity, amount, duration, diurnal cycle, covariability in space and time) and evaluation of these in models. In general models perform poorly and this provides a much-needed focus on what aspects of models are greatly in need of improvement. A substantial number of papers have now been published on these topics. The focus on the characteristics of precipitation and not just amount also provides a natural entrée into examining the extremes of precipitation, that are very important to the general public in terms of impacts. Examples are the deluges from Florence and Harvey, and extensive work has been performed on the latter. On the other side of the coin, considerable work has been performed on dry spells and drought, including improvements in a drought index. There has been considerable outreach to the general public, especially through the media, but also via talks and lectures, and a strong educational component at SUNYA. For example, the Harvey paper has an altimetrics rating of 827 and is the highest for any paper from that journal, it is at the 99th percentile of all papers of the same age, and is in the top 95 percent of all papers. The energy cycle and ocean heat content has been another focus. The entire atmospheric energy cycle has been reformulated and applied to the latest data to include effects from the enthalpy of precipitation. Relationships between ocean heat content and sea level rise have also been extensively explored in observations and models, with new insights into the role of Mount Pinatubo volcanic eruption on distorting and hiding the accelerating sea level rise. This work has been also widely featured in news media. The “year without a summer” has also been explored in the past using models, and how changes in the vertical structure and stability of the ocean with a warming climate may influence a future such volcanic event by amplifying the surface temperature response.
The Lack of a QBO-MJO Connection in Climate Models With a Nudged Stratosphere
The observed stratospheric quasi-biennial oscillation (QBO) and the tropospheric Madden-Julian oscillation (MJO) are strongly connected in boreal winter, with stronger MJO activity when lower-stratospheric winds are easterly. However, the current generation of climate models with internally generated representations of the QBO and MJO do not simulate the observed QBO-MJO connection, for reasons that remain unclear. Furthermore, this study builds on prior work exploring the QBO-MJO link in climate models whose stratospheric winds are relaxed toward reanalysis, reducing stratospheric biases in the model and imposing a realistic QBO. A series of ensemble experiments are performed using four state-of-the-art climate models capable of representing the MJO over the period 1980–2015, each with similar nudging in the stratosphere. In these four models, nudging leads to a good representation of QBO wind and temperature signals, however no model simulates the observed QBO-MJO relationship. Biases in MJO vertical structure and cloud-radiative feedbacks are investigated, but no conclusive model bias or mechanism is identified that explains the lack of a QBO-MJO connection.
Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models
Abstract. In the current global climate models (GCMs), the nonlinearity effect of subgrid cloud variations on the parameterization of warm-rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm-rain process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in situ measurements from a recent field campaign and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that tends to make EF < 1. This effect is especially strong at cloud top, where the qc and Nc correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of qc and Nc is significantly smaller than its counterpart that accounts only for the subgrid variation of qc, especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of Nc and its correlation with qc both need to be considered for an accurate simulation of the autoconversion process in GCMs.
ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation
A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.