Current Climate Simulations Research Articles

Suitable areas for temperate fruit trees in a Brazilian hotspot area: Changes driven by new IPCC scenarios

Chilling accumulation and sufficient water availability are mandatory requirements for most temperate fruit trees (TFT) productivity. Given the strong dependence of these crops on air temperature and water availability, it is imperative to analyze the impacts of climate change on TFT, especially in areas considered persistent climate change hotspots. This study analyzed the impacts of climate change on suitable areas for planting TFT in a Brazilian state considered a hotspot for future climate changes - Minas Gerais, using an Agroclimatic Zoning (AZ) method, and outlined adaptation measures for the sustainable cultivation of the main TFT under future climate scenarios. Daily near-surface air temperature (minimum and maximum) and precipitation data were used from 10 Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6), in two Shared Socioeconomic Pathways scenarios (SSP2–4.5 and SSP5–8.5). The AZ method was applied for the current climate simulations (1995–2014), as well as for projections by the near (2041–2060) and far future (2081 – 2100) climate conditions considering two base temperature values (Tb = 7ºC and 13ºC). Given intense and persistent increases in air temperature, water deficit, and reduced chilling accumulation, TFT plantations will be restricted and economically unfeasible for about 69% (SSP2.4–5) to 86% (SSP5.8–5) of Minas Gerais in the far future for both Tb values. Only ∼6% (near future) and ∼2% (far future) of the study area located in a small portion of the Southern region of Minas Gerais will be suitable for TFT plantations. However, the future of TFT plantations in places classified as regular will depend on adopting adaptation measures to reduce vulnerability to these crops, including low-chill genotypes selection, improvements in breeding programs, using strategies to break dormancy artificially, and intensifying irrigation with less water-demanding techniques.

Modeling Water Isotopes Using a Global Non‐Hydrostatic Model With an Explicit Convection: Comparison With Gridded Data Sets and Site Observations

AbstractIn this study, we developed a global cloud system‐resolving model (GCSRM) incorporating stable water isotopes (NICAM‐WISO). Using a single‐moment cloud microphysics scheme, we applied the new model to conduct a current climate simulation at a horizontal resolution of 56 km. NICAM‐WISO simulated the seasonal means of the atmospheric hydrological cycle, as well as water isotopic ratios of precipitation and vapor. The model captured the general features of precipitation isotope effects and its spatial correlations were comparable to those of other isotope‐incorporated global atmospheric models. The model showed better spatial correlation between simulated and observed values for a fine‐horizontal‐resolution (14 km) simulation compared to coarse‐horizontal‐resolution (56 km) simulation. However, the model had isotopic biases in tropical mid‐troposphere, ocean, and cold continental regions. A comparison of stable water isotopes between the simulation and observations offered clues for improving the model. For example, in the tropical mid‐troposphere, we found a negative bias in the mixing ratio and isotopic ratio of water vapor. Our analysis using satellite retrievals revealed that these underestimations were caused by weak mixing with the boundary layer vapor and low raindrop evaporation with a small evaporation fraction. The underestimations indicated weak shallow convective mixing in the model, inducing negative bias in the mixing ratio and isotopic ratio of the mid‐tropospheric vapor. These biases were also seen in the fine horizontal‐resolution simulation. Furthermore, we conducted several km‐scale atmospheric isotope circulation simulations using NICAM‐WISO. We expect that global‐scale fine‐horizontal‐resolution simulations using isotope‐incorporated GCSRMs will improve our understanding of the atmospheric hydrological cycle.

Constraining the complex refractive index of black carbon particles using the complex forward-scattering amplitude

Black carbon is the largest contributor to global aerosol’s shortwave absorption in the current atmosphere and is an important positive climate forcer. The complex refractive index, m = mr + imi, the primary determinant of the absorbed and scattered energies of incident radiation per unit volume of particulate material, has not been accurately known for atmospheric black carbon material. An accurate value at visible wavelengths has been difficult to obtain due to the black carbon’s wavelength-scale irregularity and variability of aggregate shape, distribution in particle size, and mixing with other aerosol compounds. Here, we present a method to constrain a plausible (mr, mi) domain for black carbon from the observed distribution of the complex forward-scattering amplitude S(0°). This approach suppresses the biases due to the above-mentioned complexities. The S(0°) distribution of black carbon is acquired by performing single particle S(0°) measurements in a water medium after collecting atmospheric aerosols into water. We demonstrate the method operating at λ = 0.633 μm for constraining the refractive index of black carbon aerosols in the north-western Pacific boundary layer. From the plausible (mr, mi) domain consistent with the observed S(0°) distributions and the reported range of mass absorption cross-section, we conservatively select 1.95 + 0.96i as a recommendable value of the refractive index for uncoated black carbon at visible wavelengths. The recommendable value is 0.17 larger in mi than the widely used value 1.95 + 0.79i in current aerosol-climate models, implying a ∼16% underestimate of shortwave absorption by black carbon aerosols in current climate simulations.

Drivers of uncertainty in precipitation frequency under current and future climate – application to Maryland, USA

The intensity duration frequency (IDF) curves that inform engineering design must reflect the effects of changing climate on extreme precipitation events. These changes can be addressed, in part, by statistically assessing synthetic data/outputs from high-resolution climate model projections to develop IDF curves. The estimation of IDF curves is associated with multiple sources of uncertainty. Most studies that characterize uncertainty in IDF curves under climate change only address the uncertainty due to the choice and processing of climate model outputs, but neglect uncertainty from statistical modeling choices. This study assesses the uncertainty introduced by developing IDF curves for Maryland, USA from model simulations of current and future climate, using statistical methods that are used in U.S. practice. The study analyzes output time series from the North American Regional Climate Change Assessment Program (NARCCAP) suite, which consists of 6 Atmosphere-Ocean General Circulation Models, each spatially downscaled with two different Regional Climate Models. In a separate study, the 3-hour NARCCAP model output was temporally downscaled to 15 min using two different machine learning models. Uncertainty is assessed both across and within models. Across-model uncertainty arises from the differences among synthetic time-series for precipitation and other meteorological variables produced by the 12 NARCCAP climate model projections. Within-model uncertainty arises from the modeling choices used to develop statistical IDF curves from a single climate model time series. These choices include: temporal downscaling method, time-series type, distribution, and parameter estimation method. The choice of climate model (across-model uncertainty) is the dominant contributor under both current and future climate conditions. On the within-model level, the other sources of uncertainty contribute differently for different climate models. The development and application of future-climate IDF curves should acknowledge the uncertainty introduced by statistical modeling choices, as well as by variation among climate model projections.

Assessing distribution changes of selected native and alien invasive plant species under changing climatic conditions in Nyeri County, Kenya.

The role of climate change in enhancing bio-invasions in natural environments needs to be assessed to provide baseline information for effective species management and policy formulations. In this study, potential habitat suitability maps were generated through Ecological Niche Modeling for five problematic alien and native species in current and future climate simulations for the periods 2050s and 2070s under RCP2.6, RCP4.5, and RCP8.5 emission scenarios. Projected current binary suitability maps showed that 67%, 40%, 28%, 68%, and 54% of the total study area ~ 3318 Km2 is suitable for C. decapetala, L. camara, O. stricta, S. didymobotrya and S. campylacanthum species, respectively. Assuming unlimited species dispersal, two of these species, C. decapetala and S. didymobotrya, were observed to have consistent gradual increase in potential habitats and no habitat losses under the three RCPs by the end of the 2050 and 2070 future periods. The highest recorded relative potential habitat increase was observed for O. stricta at ~205% under RCP2.6 and ~223% under RCP8.5. Although L. camara and O. stricta were observed to have habitat losses, the losses will be very low as compared to that of S. campylacanthum. L. camara and O. stricta relative habitat losses were predicted to be between ~1% under RCP2.6 to ~4.5% under RCP8.5 by 2070 while that of S. campylacanthum was between ~50% under RCP2.6 to ~68% under RCP8.5 by the year 2070. From this study we conclude that the target study species are expected to remain a big threat to inhabited areas as well as biodiversity hotspot areas especially in the Mt. Kenya and the Aberdare forest and national park reserves under climate change. The information generated through this study can be used to inform policy on prioritizing management of these species and subsequent determination of their absolute distributions within the area.

Correcting Systematic Bias in Climate Model Simulations in the Time‐Frequency Domain

AbstractSystematic biases in General Circulation Model (GCM) simulations require some adjustment before their use in change assessment and adaptation management studies. GCM simulations of the Coupled Model Intercomparison Project 6, although outperform the previous generations of GCMs, exhibit persistent biases in magnitude, variability, and frequency across a range of variables of interest. Here, we propose a novel continuous wavelet‐based bias correction (CWBC) approach to address such biases in the time‐frequency domain. The correction focuses on the magnitude and frequency of the modeled time series, as interpreted via the time‐varying spectrum ascertained using the continuous wavelet transform. The approach is applied to correct systematic biases in the time series of Niño 3.4 sea surface temperature and Arctic sea‐ice extent. The application of CWBC successfully reproduces observed attributes in the bias‐corrected time series of both climate variables for the current climate simulation along with providing a sensible projection for the future.

Link Between Opaque Cloud Properties and Atmospheric Dynamics in Observations and Simulations of Current Climate in the Tropics, and Impact on Future Predictions

AbstractUsing spaceborne lidar observations and reanalyzes (2008–2014), we relate the vertical wind speed at 500 hPa (ω500), indicator of atmospheric circulation, to properties of opaque clouds (altitude and cover) and to the Cloud Radiative Effect (CRE) in the Tropics. We confront those observations with simulations by IPSL‐CM6 and CESM1 climate models using early 21st century emissions. Both models overestimate the average opaque cloud cover. IPSL‐CM6 puts high opaque clouds too high (+2 km), especially in ascendance. CESM1 overestimates the intermediate opaque cloud cover and underestimates small and large opaque cloud covers. Both models agree that cloud properties behave differently at wind speed above (strong subsidence) or below (weak subsidence and ascendance) 20 hPa/day. In future climate (2089–2095), variables affected by biases in current climate are affected by notable changes: IPSL‐CM6 puts high opaque clouds even higher (+2 km) while opaque cloud cover above 30% decreases and below 30% increases in CESM1. Both models predict very little change in the average net CRE in the future. We find that predicted changes of cloud properties can be regionally driven by dynamic or thermodynamic changes, depending on the relationship between opaque cloud altitude and ω500 in the model. Overall, most changes are due to thermodynamic changes in the relationship between cloud property and atmospheric dynamics.

A Signal Processing Approach to Correct Systematic Bias in Trend and Variability in Climate Model Simulations

AbstractBias correction of General Circulation Model (GCM) is now an essential part of climate change studies. However, the climate change trend has been overlooked in majority of bias correction approaches. Here, a novel signal processing‐based approach for correcting systematic biases in the time‐varying trend of GCM simulations is proposed. The approach corrects for systematic deviations in spectral attributes of raw GCM simulations using discrete wavelet transforms. The order one and two moments of the underlying trend represented by the lowest frequency of wavelet component are corrected to ensure continuity in the corrected time series from the current to the future simulation period. The approach is applied to correct two data sets that exhibit opposite time‐varying trends representing the global mean sea level (GMSL) and the Arctic sea‐ice extent. Results indicate that bias in trend is corrected, while continuity in time and observed variability at all frequencies in current climate simulations are maintained.

A new perspective on evaluating high-resolution urban climate simulation with urban canopy parameters

The ‘state-of-the-art’ urban climate models have not been evaluated against dense meteorological networks under various weather conditions. In this study, we conducted high-resolution urban climate simulations in Beijing and investigated the relationship between their performances and urban canopy parameters (UCPs). The latest version of single-layer (UCM) and multi-layer (BEP) urban canopy models were tested separately. Results show that model performances are insensitive to rainfall events in summer, but change significantly with wind conditions in winter. Inclusion of UCPs has a limited impact on air temperature simulations. In terms of wind speed simulations, consistent overestimations by UCM and BEP are found in both summer and winter. The overestimation by BEP reduces significantly when UCPs are available, especially on windy days in winter. On the other hand, performance of UCM can degrade over urban areas with canopy parameters. Wind speed biases are found to correlate significantly with UCPs. The accuracy of wind speed simulations by UCM and BEP increases with urban fraction and building surface ratio. This indicates poor model performances over low-density urban areas, which requires enhanced aerodynamic parameterizations to better account for UCPs. This study reveals the limitation of current urban climate simulations and provides guidance for future model developments.

Snowfall and snowpack in the Western U.S. as captured by convection permitting climate simulations: current climate and pseudo global warming future climate

This study examines current and future western U.S. snowfall and snowpack through current and future climate simulations with a 4-km horizontal grid spacing cloud permitting regional climate model over the entire CONtinental U.S. for a 13-year period between 2001 and 2013. At this horizontal resolution, the spatiotemporal distribution of the orographic snowfall and snowpack is well captured partly due to the ability of the model to realistically simulate mesoscale and microphysical features such as orographically induced updrafts driving clouds and precipitation. The historical simulation well captures the observed snowfall and snowpack amounts and pattern in the western U.S. The future climate simulation uses the Pseudo-Global Warming approach, taking the climate change signal from CMIP5 multi-model ensemble-mean difference between 2070–2099 and 1976–2005. The results show that the thermodynamic impacts of climate change in the western U.S. can be characterized considering mountain ranges in two distinct geographic regions: the mountain ranges close to the Pacific Ocean (coastal ranges) and those in the inter-mountain west. Climate change out to 2100 significantly impacts all aspects of the water cycle, with pronounced climate change response in the coastal ranges. A notable result is that the snowpack in the Pacific Northwest is predicted to decrease by ~ 70% by 2100. Trends of this magnitude have already been observed in the historical data and in previous studies. The current Pseudo Global Warming future climate simulation and previous global climate simulations all suggest that these trends will continue to the point that most snowpack will be gone by 2100 in the Pacific Northwest for the most aggressive RCP8.5 climate scenario, even if annual precipitation increases by 10%. Future work will focus on extending the current convective permitting results to a full climate change simulation allowing for dynamical changes in the flow.

Impact of climate change on intense Bay of Bengal tropical cyclones of the post-monsoon season: a pseudo global warming approach

Tropical cyclones (TCs) that make landfall over India’s east coast are responsible for significant loss of life along affected coastlines. TCs forming over the Bay of Bengal (BoB) region in October, November, and December have, in the past, intensified significantly at the time of landfall. The effects of climate change on TCs of different strengths and their characteristics such as track, intensity, precipitation, and convective available potential energy over the BoB region have not been well studied. This study explores the effects of climate change on two TCs of very severe cyclonic storm (VSCS) category (TC Vardah and TC Madi), and two other TCs of extremely severe cyclonic storm (ESCS) category (TC Hudhud and TC Phailin) formed over BoB, both in the short term (2035) and long term (2075). The high-resolution Weather Research and Forecasting (WRF) model is used to simulate the TCs under current and future climate conditions. The simulated TC track and intensity in the current climate agree well with the observations. To explore the impacts of climate change on TCs, the mean climate change signal, computed from future projections of the Community Climate System Model (CCSM4) in different representative concentration pathway (RCP) scenarios, is added to current climate conditions by using the pseudo global warming method. Results show a climate change-related reduction in TC translation speed, deepening of TC core, increased maximum surface wind, and increased precipitation over land in future RCP (4.5, 6.0, and 8.5) scenarios. The TCs in future RCP scenarios are seen to be more intensified compared to current climate simulations. Results demonstrate that all VSCS and ESCS category TCs considered in this study are likely to further intensify to the next higher category level with respect to their current classification, particularly in the far future RCP 6.0 and in the far future RCP 8.5 scenarios. The cyclone damage potential index of TC Vardah, TC Hudhud, and TC Phailin is projected to increase in a future warming climate.

Kilometer-scale modeling projects a tripling of Alaskan convective storms in future climate

Convective storms produce heavier downpours and become more intense with climate change. Such changes could be even amplified in high-latitudes since the Arctic is warming faster than any other region in the world and subsequently moistening. However, little attention has been paid to the impact of global warming on intense thunderstorms in high latitude continental regions, where they can produce flash flooding or ignite wildfires. We use a model with kilometer-scale grid spacing to simulate Alaska’s climate under present and end of the century high emission scenario conditions. The current climate simulation is able to capture the frequency and intensity of hourly precipitation compared to rain gauge data. We apply a precipitation tracking algorithm to identify intense, organized convective systems, which are projected to triple in frequency and extend to the northernmost regions of Alaska under future climate conditions. Peak rainfall rates in the core of the storms will intensify by 37% in line with atmospheric moisture increases. These results could have severe impacts on Alaska’s economy and ecology since floods are already the costliest natural disaster in central Alaska and an increasing number of thunderstorms could result in more wildfires ignitions.

Impact of downscaled rainfall biases on projected runoff changes

Abstract. Realistic projections of changes to daily rainfall frequency and magnitude, at catchment scales, are required to assess the potential impacts of climate change on regional water supply. We show that quantile–quantile mapping (QQM) bias-corrected daily rainfall from dynamically downscaled WRF simulations of current climate produce biased hydrological simulations, in a case study for the state of Victoria, Australia (237 629 km2). While the QQM bias correction can remove bias in daily rainfall distributions at each 10 km × 10 km grid point across Victoria, the GR4J rainfall–runoff model underestimates runoff when driven with QQM bias-corrected daily rainfall. We compare simulated runoff differences using bias-corrected and empirically scaled rainfall for several key water supply catchments across Victoria and discuss the implications for confidence in the magnitude of projected changes for mid-century. Our results highlight the imperative for methods that can correct for temporal and spatial biases in dynamically downscaled daily rainfall if they are to be suitable for hydrological projection.

Understanding future changes in tropical cyclogenesis using Self-Organizing Maps

Future changes in tropical cyclone (TC) genesis locations and frequency are explored by identifying relationships between TC genesis and dominant daily large-scale patterns, and evaluating the strength of these relationships under a climate change scenario. Self-Organizing Maps (SOMs) are used to characterize the dominant large-scale patterns in reanalysis data and in a regional climate model ensemble simulation of current climate. The main features on the resulting sea level pressure (SLP) SOMs are nodes that resemble both the negative and positive phases of the North Atlantic Oscillation, as well as blocking and ridging regimes. The frequency of the NAO-like nodes is strongly linked to TC genesis frequency and preferred genesis locations. This link is used to develop a statistical relationship between the frequency of large scale SLP patterns and TC genesis. The application of this relationship to an ensemble regional climate simulation under a future climate forcing scenario predicts fewer TCs, which is consistent with the regional climate model that explicitly simulates fewer TCs. This demonstrates the strength of the relationships and their use in assessing future changes in TC genesis locations and frequency.

Simulating North American Weather Types With Regional Climate Models

Regional Climate Models (RCMs) are able to simulate small-scale processes that are missing in their coarser resolution driving data and thereby provide valuable climate information for climate impact assessments. Less attention has been paid to the ability of RCMs to capture large-scale weather types (WTs). An inaccurate representation of WTs can result in biases and uncertainties in current and future climate simulations that cannot be easily detected by standard model evaluation metrics. Here we define 12 hydrologically important WTs in the Contiguous United States (CONUS). We test if RCMs from the North American CORDEX (NA-CORDEX) and the Weather Research and Forecasting (WRF) model large physics ensembles (WRF36) can capture those WTs in the current climate and how they simulate changes in the future. Our results show that the NA-CORDEX RCMs are able to simulate WTs more accurately than members of the WRF36 ensemble. The much larger WRF36 domain in combination with not constraining large-scale conditions by spectral nudging results in lower WT skill. The selection of the driving global climate model (GCM) has a large effect on the skill of NA-CORDEX simulations but a smaller impact on the WRF36 runs. The formulation of the RCM is of minor importance except for capturing the variability within WTs. Changing the model physics or increasing the RCM horizontal grid spacing has little effect. These results highlight the importance of selecting GCMs with accurate synoptic-scale variability for downscaling and to find a balance between large domains that can result in biased WT representations and small domains that inhibit the realistic development of mesoscale processes. At the end of the century, monsoonal flow conditions increase systematically by up to 30 % and a WT that is a significant source of moisture for the Northern Plains during the growing seasons decreases systematically up to -30 %.

Interaction of urban heat islands and heat waves under current and future climate conditions and their mitigation using green and cool roofs in New York City and Phoenix, Arizona

Urban environments and heat waves interact synergistically and aggravate the thermal environment through the urban heat island (UHI) effect. Of concern is the potential for a projected warmer future climate to further compound heat waves in urban environments. The present study investigates the interaction of a 2006 heat wave in North America with two urban environments (Phoenix and New York City (NYC)) in current climate and future climate simulations. The future climate conditions were generated using the pseudo-global warming methodology. Multiple high-resolution (3 km) simulations were performed using the weather research and forecasting (WRF) model coupled with the single-layer urban canopy model to improve representation of urban processes and we explore how irrigated green roofs and cool roofs can mitigate heat wave amplification by UHIs. To quantify heat wave intensity, an analytical model is applied to the WRF model output that considers the urban surface heat and water vapor exchanges with the atmosphere. A future, warmer climate is found to amplify the UHI intensity during heat waves in both Phoenix (21%) and NYC (48%), but the amplification is of great uncertainty as its magnitude is comparable to the temporal variability of temperatures. The increase in urban heat index can be almost completely offset by adopting irrigated green roofs in urban areas, and partially offset by adopting cool roofs.

Intermodel Differences in Upwelling in the Tropical Tropopause Layer Among CMIP5 Models

AbstractThe climatology of upwelling in the tropical tropopause layer (TTL) in current climate simulations and in future climate projections is examined using models participated in the Coupled Model Intercomparison Project phase 5 (CMIP5). Large intermodel differences in upwelling in the TTL appear in the current climate simulations. Model composite analysis and upwelling diagnosis based on the zonal momentum budget indicate that the intermodel differences in upwelling are controlled by meridional eddy momentum fluxes associated with tropical planetary waves and midlatitude synoptic waves. Future climate simulations indicate that upwelling changes in the TTL are significantly correlated with the upwelling in current climate simulations. Models with strong (weak) TTL upwelling in the current climate simulations tend to project strong (weak) upwelling enhancement in the future climate. The intermodel differences in the upwelling change arise from the same dynamical factors as the current climate cases. The contribution of sea surface temperature (SST) to the intermodel upwelling differences is examined by SST‐prescribed simulations in CMIP5. The contribution of intermodel SST differences to the upwelling is smaller than that of intrinsic atmospheric intermodel differences. The significant correlation of the tropical upwelling between the current climate simulations and the future changes appears to be independent of the target latitude range.

Projections of southern hemisphere tropical cyclone track density using CMIP5 models

A recently validated algorithm for detecting and tracking tropical cyclones (TCs) in coarse resolution climate models was applied to a selected group of 12 models from the Coupled Model Intercomparison Project (CMIP5) to assess potential changes in TC track characteristics in the Southern Hemisphere (SH) due to greenhouse warming. Current-climate simulations over the period 1970–2000 are first evaluated against observations using measures of TC genesis location and frequency, as well as track trajectory and lifetime in seven objectively defined genesis regions. The 12-model (12-M) ensemble showed substantial skill in reproducing a realistic TC climatology over the evaluation period. To address potential biases associated with model interdependency, analyses were repeated with an ensemble of five independent models (5-M). Results from both the 12-M and 5-M ensembles were very similar, instilling confidence in the models for climate projections if the current TC-climate relationship is to remain stationary. Projected changes in TC track density between the current- and future-climate (2070–2100) simulations under the Representatives Concentration 8.5 Pathways (RCP8.5) are also assessed. Overall, projection results showed a substantial decrease (~ 1–3 per decade) in track density over most parts of the SH by the end of the twenty-first century. This decrease is attributed to a significant reduction in TC numbers (~ 15–42%) consistent with changes in large-scale environmental parameters such as relative vorticity, environmental vertical wind shear and relative humidity. This study may assist with adaption pathways and implications for regional-scale climate change for vulnerable regions in the SH.

Estimates of the Change in the Oceanic Precipitation Off the Coast of Europe due to Increasing Greenhouse Gas Emissions

This paper presents a consensus estimate of the changes in oceanic precipitation off the coast of Europe under increasing greenhouse gas emissions. An ensemble of regional climate models (RCMs) and three gauge and satellite-derived observational precipitation datasets are compared. While the fit between the RCMs’ simulation of current climate and the observations shows the consistency of the future-climate projections, uncertainties in both the models and the measurements need to be considered to generate a consensus estimate of the potential changes. Since oceanic precipitation is one of the factors affecting the thermohaline circulation, the feedback mechanisms of the changes in the net influx of freshwater from precipitation are relevant not only for improving oceanic-atmospheric coupled models but also to ascertain the climate signal in a global warming scenario.

Could Machine Learning Break the Convection Parameterization Deadlock?

AbstractRepresenting unresolved moist convection in coarse‐scale climate models remains one of the main bottlenecks of current climate simulations. Many of the biases present with parameterized convection are strongly reduced when convection is explicitly resolved (i.e., in cloud resolving models at high spatial resolution approximately a kilometer or so). We here present a novel approach to convective parameterization based on machine learning, using an aquaplanet with prescribed sea surface temperatures as a proof of concept. A deep neural network is trained with a superparameterized version of a climate model in which convection is resolved by thousands of embedded 2‐D cloud resolving models. The machine learning representation of convection, which we call the Cloud Brain (CBRAIN), can skillfully predict many of the convective heating, moistening, and radiative features of superparameterization that are most important to climate simulation, although an unintended side effect is to reduce some of the superparameterization's inherent variance. Since as few as three months' high‐frequency global training data prove sufficient to provide this skill, the approach presented here opens up a new possibility for a future class of convection parameterizations in climate models that are built “top‐down,” that is, by learning salient features of convection from unusually explicit simulations.