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Previous work has shown that estimates of climate sensitivity vary over time in response to abrupt CO2 forcing in climate model simulations. The energy fluxes that drive warming in response to increasing CO2 also influence precipitation, which prompts the question: Does the precipitation response therefore also vary over time? We investigate by examining the response of precipitation to warming forced by greenhouse gases—the hydrological sensitivity—in a set of millennial‐length climate simulations with multiple climate models, Long Run Model Intercomparison Project (LongRunMIP). We compare hydrological sensitivity calculated from three different timescales of the simulations: years 1–20, 21–150, and 151–1000. We show that the hydrological sensitivity lacks a consistent dependence on timescale, in contrast to climate sensitivity. Decomposition of the surface energy budget reveals that the relative muting of the multi‐model mean hydrological sensitivity is driven by surface downwelling shortwave flux. Plain Language Summary: Previous work has shown that when carbon dioxide concentrations are abruptly quadrupled, the response of the global energy imbalance between the Earth and space is a fast change for the first couple of decades, followed by a slower response as time goes on. Precipitation is also an important part of the energy budget, so perhaps its response to increasing carbon dioxide might depend on timescale as well. This study investigates using a recent set of very long model simulations. We find that there is less of a dependence for precipitation than for the global energy imbalance. Because the effect is smaller, it is also more subject to the details of the statistical analysis, so particular attention is paid there. Key Points: The trend across timescales of hydrological sensitivity is less consistent and has a smaller magnitude compared to climate sensitivityDownwelling surface shortwave flux is the component of the energy budget that contributes most consistently to this difference [ABSTRACT FROM AUTHOR]

The width of the Inter-Tropical Convergence Zone (ITCZ) affects tropical rainfall, Earth's albedo, large-scale circulation, and climate sensitivity. To better understand the ITCZ width and its effects on global climate, we present a protocol to force changes in ITCZ width in climate models. Starting from an aquaplanet configuration with a slab ocean, adding surface heat fluxes in the deep tropics forces the ITCZ to narrow, and subtracting them causes it to widen. The protocol successfully generates changes in ITCZ width in all four climate models used in this study. ITCZ width in each model responds linearly to forcing magnitude and sign. Comparing across the four climate models, a response to varying ITCZ width that is remarkably consistent among models is the ITCZ strength, which is greater the narrower the ITCZ. On the other hand, the effect of varying ITCZ width on climate sensitivity is divergent among our four models, varying even in sign. Results from this pilot study highlight the connections between surface fluxes, ITCZ width, and the wider climate. A comprehensive model intercomparison project (MIP) has the potential to advance understanding of both the physical processes shaping ITCZ width and its influence on remote atmospheric circulations and global climate. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance. Plain Language Summary: As global temperatures rise, there's growing concern about an increase in the frequency of extreme precipitation events. However, projections of future changes to large‐magnitude precipitation events often rely on coarse‐resolution models which simulate meteorology on a spatial scale larger than most storms. To address this, we explored the potential of higher resolution models from the Coupled Model Intercomparison Project HighResMIP ensemble to improve the fidelity of simulated precipitation variability across the western United States. We compared four HighResMIP models with their coarse‐resolution counterparts from 1950 to 2014 and found that coarse‐resolution models consistently underestimated the dominance of large storms compared to observed data. While some HighResMIP models showed improvement, others remained unchanged or even showed increased bias. This suggests that besides spatial resolution alone, factors like model structure and numerical representation of fine scale precipitation processes play a role in accurately simulating intense storms. These findings highlight the importance of considering both resolution and climate model structure in predicting future extreme precipitation events, especially in regions where precipitation is generated by processes that operate on a wide range of spatial scales. Key Points: Increasing spatial resolution of general circulation models (GCMs) does not consistently improve simulation of large‐storm dominance across the western United StatesCoarse resolution GCMs simulate large‐storm dominance well in regions where the largest daily precipitation events are due to synoptic scale precipitationModel structure and parameterization of precipitation generation appear to be equally as important as resolution in the simulation of large‐storm dominance [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

Organized mesoscale convective systems (MCSs) contribute a significant amount of precipitation in the Central and Eastern US during spring and summer, which impacts the availability of freshwater and flooding events. However, current global Earth system models cannot capture MCSs well and misrepresent the statistics of precipitation in the region. In this study, we investigate the representation of MCSs in three configurations of the Energy Exascale Earth System Model (E3SMv1) by tracking individual storms based on outgoing longwave radiation using a new application of TempestExtremes. Our results indicate that conventional parameterizations of convection, implemented in both low (LR; ∼150 km) and high (HR; ∼25 km) resolution configurations, fail to capture almost all MCS‐like events, in‐part because they underestimate high‐level cloud ice associated with deep convection. On the other hand, the multiscale modeling framework (MMF; cloud‐resolving models embedded in each grid‐column of ∼150 km resolution E3SMv1) configuration represents MCSs and their annual cycle better. Nevertheless, relative to observations, the E3SMv1‐MMF spatial distribution of MCSs and associated precipitation is shifted eastward, and the diurnal timing is lagged. A comparison between the large‐scale environment in E3SMv1‐MMF and ERA5 reanalysis suggests that the biases during the summer in E3SMv1‐MMF are associated with biases in low‐level humidity and meridional moisture transport within the low‐level jet. The fact that conventional parameterizations of convection, even with high‐resolution, cannot capture MCSs over the US suggests that methods with explicit representation of kilometer‐scale convective organization, such as the MMF, may be necessary for improving the simulation of these convective systems. Plain Language Summary: Organized thunderstorms, known as mesoscale convection systems (MCSs), can contribute to large amounts of rainfall and flooding in the United States, especially during the summer and spring. However, current Earth system models have difficulties representing the complex physical processes that are important to these storm systems, which occur at smaller scales than the models resolve. In this study, we assess the ability of the Energy Exascale Earth System Model v1 to simulate MCSs using two advanced configurations (high‐resolution and multiscale modeling framework), and compare to the conventional low‐resolution configuration and observations. To identify MCS‐like events, we implemented a tracking method based on only the outgoing longwave radiation, which we apply to assess MCS characteristics and associated precipitation. Our results suggest conventionally parameterized configurations, with low‐ (∼150 km) and high‐ (∼25 km) resolution grids, fail to capture MCSs, due in part to an under‐simulation of high‐level cloud ice. On the other hand, the multiscale modeling framework configuration (i.e., kilometer‐scale cloud‐resolving models embedded in a standard ∼150 km resolution global grid) better simulates MCSs and related characteristics. Nevertheless, we identify biases in the location and timing of simulated MCSs and associated rainfall, which can be linked to biases in simulating the large‐scale environment during summer. Key Points: Energy Exascale Earth System Model (E3SMv1) with conventional parameterizations of convection underestimates high‐level cloud ice and fails to capture spring and summer mesoscale convective systems (MCSs)E3SMv1 with the MMF configuration captures MCSs, but has some biases, including an eastward shift in location and lag in diurnal timingE3SMv1‐MMF MCS‐precipitation biases are linked to biases in low‐level humidity and moisture transport associated with the low‐level jet [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth System Models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The PCMDI Metrics Package (PMP) is an open-source Python software package that provides 'quick-look' objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including extremes. The PMP results are produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases. An important priority of the PMP is to document evaluation statistics for all Historical and AMIP simulations submitted to recent phases of CMIP, providing version-controlled information for all data sets and software packages being used. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. In this paper, we present an overview of the PMP including its history to date, capabilities, recent updates, and future direction. [ABSTRACT FROM AUTHOR]

Apparent hydrological sensitivity (ηa), the change in the global mean precipitation per degree K of global surface warming, is a key aspect of the climate system's response to increasing CO2 forcing. To determine whether ηa depends on the forcing amplitude we analyze idealized experiments over a broad range of abrupt CO2 forcing, from 2× to 8× preindustrial values, with two distinct climate models. We find little change in ηa between 2× and 4×CO2, and almost no change beyond 5×CO2. We validate this finding under transient CO2 forcing at 1%‐per‐year, up to 8×CO2. We further corroborate this result by analyzing the 1%‐per‐year output of more than 15 CMIP5/6 models. Lastly, we examine the 1,000‐year long LongrunMIP model output, and again find little change in ηa. This wealth of results demonstrates that ηa is a very weak function of CO2 forcing. Plain Language Summary: Hydrological sensitivity (HS) is defined as the change in globally‐averaged precipitation per degree K of surface temperature increase caused by increasing concentrations of greenhouse gasses, such as CO2. It is important to understand how HS changes with different levels of CO2 in the atmosphere. To do this we analyzed model experiments with varying increases of CO2. We find little change in HS between 2× to 4× the pre‐industrial levels of CO2, and almost no change beyond 5×CO2. Additionally, we analyze model experiments where CO2 concentrations increase by 1% per year and see similar results. Finally, we validate this finding with models with significantly longer run times. We thus conclude that HS is independent of the level of CO2 in the atmosphere. Key Points: We examine the dependence of apparent hydrological sensitivity (ηa, defined as ΔP/ΔT) on the magnitude of CO2 forcingWe find little change in ηa in abrupt 2× to 8×CO2 experiments and a transient 1%/year experiment up to 8×CO2Little change in ηa, notably at large CO2, is also found in most CMIP5, CMIP6, and Longrun‐MIP models [ABSTRACT FROM AUTHOR]

As the resolution of global Earth system models increases, regional-scale evaluations are becoming ever more important. This study presents a framework for quantifying precipitation distributions at regional scales and applies it to evaluate Coupled Model Intercomparison Project (CMIP) 5 and 6 models. We employ the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6) climate reference regions over land and propose refinements to the oceanic regions based on the homogeneity of precipitation distribution characteristics. The homogeneous regions are identified as heavy-, moderate-, and light-precipitating areas by K -means clustering of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) version 6 final run product (IMERG) precipitation frequency and amount distributions. With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions; the similarity between observed and modeled frequency distributions; an unevenness measure based on cumulative amount; average total intensity on all days with precipitation; and number of precipitating days each year. We apply our framework to 25 CMIP5 and 41 CMIP6 models, as well as six observation-based products of daily precipitation. Our results indicate that many CMIP5 and 6 models substantially overestimate the observed light-precipitation amount and frequency, as well as the number of precipitating days, especially over midlatitude regions outside of some land regions in the Americas and Eurasia. Improvement from CMIP5 to 6 is shown in some regions, especially in midlatitude regions, but it is not evident globally, and over the tropics most metrics point toward degradation. [ABSTRACT FROM AUTHOR]

Two decades of high-resolution satellite observations and climate modeling studies have indicated strong ocean–atmosphere coupled feedback mediated by ocean mesoscale processes, including semipermanent and meandrous SST fronts, mesoscale eddies, and filaments. The air–sea exchanges in latent heat, sensible heat, momentum, and carbon dioxide associated with this so-called mesoscale air–sea interaction are robust near the major western boundary currents, Southern Ocean fronts, and equatorial and coastal upwelling zones, but they are also ubiquitous over the global oceans wherever ocean mesoscale processes are active. Current theories, informed by rapidly advancing observational and modeling capabilities, have established the importance of mesoscale and frontal-scale air–sea interaction processes for understanding large-scale ocean circulation, biogeochemistry, and weather and climate variability. However, numerous challenges remain to accurately diagnose, observe, and simulate mesoscale air–sea interaction to quantify its impacts on large-scale processes. This article provides a comprehensive review of key aspects pertinent to mesoscale air–sea interaction, synthesizes current understanding with remaining gaps and uncertainties, and provides recommendations on theoretical, observational, and modeling strategies for future air–sea interaction research. Significance Statement: Recent high-resolution satellite observations and climate models have shown a significant impact of coupled ocean–atmosphere interactions mediated by small-scale (mesoscale) ocean processes, including ocean eddies and fronts, on Earth's climate. Ocean mesoscale-induced spatial temperature and current variability modulate the air–sea exchanges in heat, momentum, and mass (e.g., gases such as water vapor and carbon dioxide), altering coupled boundary layer processes. Studies suggest that skillful simulations and predictions of ocean circulation, biogeochemistry, and weather events and climate variability depend on accurate representation of the eddy-mediated air–sea interaction. However, numerous challenges remain in accurately diagnosing, observing, and simulating mesoscale air–sea interaction to quantify its large-scale impacts. This article synthesizes the latest understanding of mesoscale air–sea interaction, identifies remaining gaps and uncertainties, and provides recommendations on strategies for future ocean–weather–climate research. [ABSTRACT FROM AUTHOR]

Future projected changes in precipitation substantially impact societies worldwide. However, large uncertainties remain due to sparse historical observational coverage, large internal climate variability, and climate model disagreement. Here, we present a novel reconstruction of seasonally averaged zonal precipitation metrics from sparse rain‐gauge data using regularized regression techniques that are trained across climate model simulations. Subsequently, we test the reconstruction on independent satellite data and reanalyzed precipitation, and find a large fraction of historical zonal mean precipitation (ZMP) variability is recovered, in particular over the Northern hemisphere and in parts of the tropics. Finally, we demonstrate that the reconstructed ZMP trends are outside the variability of pre‐industrial control simulations, and are largely consistent with the range of historical simulations driven by external forcing. Overall, we illustrate a novel way of estimating seasonally averaged zonal precipitation from gauge data, and trends therein that show a signal very likely caused by human influence. Plain Language Summary: When studying changes in the global water cycle due to climate change it is instructive to study precipitation along constant latitudes (zonal mean), as the average amount and seasonality of precipitation differ strongly across latitudes. When trying to calculate the zonal mean from observations, we face the problem that observations do not exist for many locations at the latitude in question since there may be no precipitation gauges, and the number and locations of gauge stations changes over time. Here we present a method to reconstruct the zonal mean precipitation (ZMP) from spatially incomplete observations, by training a statistical model to predict the zonal mean from only the observed grid cells directly. Our reconstructions show high similarity to satellite‐based estimates of ZMP. Further, we find a trend in these reconstructions when analyzing the pattern of all zonal trends together, which is very likely caused by human influence. Key Points: Detection and attribution of change in the water cycle is difficult due to sparse observations, model uncertainty, and internal variabilityWe reconstruct the inter‐annual variability of zonal mean precipitation from gauge data using regularized regression techniquesWe demonstrate that the observed multi‐decadal zonal water cycle changes lie within the range of historical climate model simulations [ABSTRACT FROM AUTHOR]

A framework for quantifying precipitation distributions at regional scales is presented and applied to CMIP 5 and 6 models. We employ the IPCC AR6 climate reference regions over land and propose refinements to the oceanic regions based on the homogeneity of precipitation distribution characteristics. The homogeneous regions are identified as heavy, moderate, and light precipitating areas by K-means clustering of IMERG precipitation frequency and amount distributions. With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions, the similarity between observed and modeled frequency distributions, an unevenness measure based on cumulative amount, average total intensity on all days with precipitation, and number of precipitating days each year. We apply our framework to 25 CMIP5 and 41 CMIP6 models, and 6 observation-based products of daily precipitation. Our results indicate that many CMIP 5 and 6 models substantially overestimate the observed light precipitation amount and frequency as well as the number of precipitating days, especially over mid-latitude regions outside of some land regions in the Americas and Eurasia. Improvement from CMIP 5 to 6 is shown in some regions, especially in mid-latitude regions, but it is not evident globally, and over the tropics most metrics point toward over degradation. [ABSTRACT FROM AUTHOR]

A framework for quantifying precipitation distributions at regional scales is presented and applied to CMIP 5 and 6 models. We employ the IPCC AR6 climate reference regions over land and propose refinements to the oceanic regions based on the homogeneity of precipitation distribution characteristics. The homogeneous regions are identified as heavy, moderate, and light precipitating areas by K-means clustering of IMERG precipitation frequency and amount distributions. With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions, the similarity between observed and modeled frequency distributions, an unevenness measure based on cumulative amount, average total intensity on all days with precipitation, and number of precipitating days each year. We apply our framework to 25 CMIP5 and 41 CMIP6 models, and 6 observation-based products of daily precipitation. Our results indicate that many CMIP 5 and 6 models substantially overestimate the observed light precipitation amount and frequency as well as the number of precipitating days, especially over mid-latitude regions outside of some land regions in the Americas and Eurasia. Improvement from CMIP 5 to 6 is shown in some regions, especially in mid-latitude regions, but it is not evident globally, and over the tropics most metrics point toward over degradation. [ABSTRACT FROM AUTHOR]

Extremes in temperature and precipitation are associated with damaging floods, prolonged drought, destructive wildfires, agricultural challenges, compromised human health, vulnerable infrastructure, and threatened ecosystems and species. Often, the steady and progressive trends (or presses) of rising global temperature are the central focus in how climate impacts are described. However, observations of extreme weather events (or pulses) increasingly show that the intensity, duration and/or frequency of acute events are also changing, resulting in greater impacts on communities and the environment. Describing how the influence of extreme events may shape water management in the Colorado River Basin in clear terms is critical to sound future planning and efforts to manage risk. Three scenario planning workshops in 2019 and 2020 were held as part of a Colorado River Conversations series, identifying potential impacts from multiple intersecting extreme events. Water managers identified climate‐related events of concern in the Colorado River Basin that necessitate greater attention and adaptive responses. To support efforts to include consideration of climate‐change‐driven extremes in water management and planning, we explore the current state of knowledge at the confluence of long‐term climate shifts and extreme weather in the Colorado River Basin related to the events of concern that were identified by scenario planning participants. [ABSTRACT FROM AUTHOR]

Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population. [ABSTRACT FROM AUTHOR]

Objective performance metrics that measure precipitation variability across time scales from subdaily to interannual are presented and applied to Historical simulations of Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. Three satellite-based precipitation estimates (IMERG, TRMM, and CMORPH) are used as reference data. We apply two independent methods to estimate temporal variability of precipitation and compare the consistency in their results. The first method is derived from power spectra analysis of 3-hourly precipitation, measuring forced variability by solar insolation (diurnal and annual cycles) and internal variability at different time scales (subdaily, synoptic, subseasonal, seasonal, and interannual). The second method is based on time averaging and facilitates estimating the seasonality of subdaily variability. Supporting the robustness of our metric, we find a near equivalence between the results obtained from the two methods when examining simulated-to-observed ratios over large domains (global, tropics, extratropics, land, or ocean). Additionally, we demonstrate that our model evaluation is not very sensitive to the discrepancies between observations. Our results reveal that CMIP5 and CMIP6 models in general overestimate the forced variability while they underestimate the internal variability, especially in the tropical ocean and higher-frequency variability. The underestimation of subdaily variability is consistent across different seasons. The internal variability is overall improved in CMIP6, but remains underestimated, and there is little evidence of improvement in forced variability. Increased horizontal resolution results in some improvement of internal variability at subdaily and synoptic time scales, but not at longer time scales. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) and their associated precipitation can have devastating impacts on the areas affected, with outcomes ranging from mudslides to inland flash flooding. Previous studies have used a fixed radius around the TC to isolate storm-related precipitation. One previous study instead used a dynamic radius of 8 m s−1 winds, but the wind field of the TC can deteriorate or shift quickly after landfall or the onset of extratropical transition (ET). This study uses a dynamical radius derived from the 500-hPa geopotential height in and around the TC to define TC- and post-tropical cyclone (PTC)-related heavy precipitation, allowing for the analysis of precipitation with tropical origins after the official demise of the original TC. Climatologies are constructed, indicating a maximum in TC- and PTC-related heavy precipitation in the west North Pacific and a secondary maximum in the east North Pacific. PTC-related heavy precipitation accounts for as much as 40% of the annual heavy precipitation in the northwest portion of the west North Pacific basin and 3.13% of heavy precipitation globally. We observe that the major hurricane stage contributes on average 2.6% of the global TC- and PTC-related precipitation, while the less intense but more common tropical storm stages of the TC life cycle contribute 85.7% of this observed precipitation. This analysis framework can be further extended to assess model biases and climate projections of TC and PTC precipitation. [ABSTRACT FROM AUTHOR]

Global warming is changing the intensity distribution of daily precipitation, with an increased frequency of heavy precipitation and reduced frequency of light/moderate precipitation in general circulation model (GCM) projections. Projected future CMIP5 GCM changes in regional daily precipitation distribution can be described by a combination of two idealized modes: a frequency decrease mode, representing a reduction in the frequency of precipitation at all rain rates; and a frequency shift mode, where the distribution shifts toward heavier rain rates. A decrease in daily precipitation frequency and an increase in intensity are projected in most regions, but the magnitude of change shows large regional variations. The two modes generally capture the projected shift from light/moderate to heavy rain rates but do not recreate GCM changes at the very highest and lowest rain rates. We propose a simple framework for deep convective precipitation change based on the dry static energy (DSE) budget, which provides a physical explanation of these idealized modes in regions and seasons where deep convection dominates precipitation. One possibility is that a frequency decrease mode is driven by increased convective inhibition (CIN). In this DSE framework, increased moisture under warming could influence the shape of the precipitation intensity distribution, particularly at the highest rain rates, but does not govern the overall magnitude of the shift to heavier rain rates, which is not well described by the Clausius–Clapeyron relationship. Changes in daily regional precipitation are not free to respond only to local changes (in e.g., moisture) but are also constrained by the DSE budget, particularly by DSE transport associated with the large-scale circulation. [ABSTRACT FROM AUTHOR]

State-of-the-art climate models exhibit significant spread in the climatological value of atmospheric shortwave absorption (SWA). This study investigates both the possible causes and climatic impacts of this SWA intermodel spread. The intermodel spread of global-mean SWA largely originates from the intermodel difference in water vapor shortwave absorptivity. Hence, we alter the water vapor shortwave absorptivity in the Community Earth System Model, version 1, with the Community Atmosphere Model, version 4 (CESM1-CAM4). Increasing the water vapor shortwave absorptivity leads to a reduction in global-mean precipitation and a La Niña–like cooling over the tropical Pacific. The global-mean atmospheric energy budget suggests that the precipitation is suppressed as a way to compensate for the increased SWA. The precipitation reduction is driven by the weakened surface winds, stabilized planetary boundary layer, and surface cooling. The La Niña–like cooling over the tropical Pacific is attributed to the zonal asymmetry of climatological evaporative damping efficiency and the low cloud enhancement over the eastern basin. Complementary fixed SSTs simulations suggest that the latter is more fundamental and that it primarily arises from atmospheric processes. Consistent with our experiments, the CMIP5/6 models with a higher global-mean SWA tend to produce tropical Pacific toward a more La Niña–like mean state, highlighting the possible role of water vapor shortwave absorptivity for shaping the mean-state climate patterns. [ABSTRACT FROM AUTHOR]

A key challenge in climate science is to quantify the forced response in impact-relevant variables such as precipitation against the background of internal variability, both in models and observations. Dynamical adjustment techniques aim to remove unforced variability from a target variable by identifying patterns associated with circulation, thus effectively acting as a filter for dynamically induced variability. The forced contributions are interpreted as the variation that is unexplained by circulation. However, dynamical adjustment of precipitation at local scales remains challenging because of large natural variability and the complex, nonlinear relationship between precipitation and circulation particularly in heterogeneous terrain. Building on variational autoencoders, we introduce a novel statistical model – the Latent Linear Adjustment Autoencoder (LLAAE) – that enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner. To predict circulation-induced precipitation, the Latent Linear Adjustment Autoencoder combines a linear component, which models the relationship between circulation and the latent space of an autoencoder, with the autoencoder's nonlinear decoder. The combination is achieved by imposing an additional penalty in the cost function that encourages linearity between the circulation field and the autoencoder's latent space, hence leveraging robustness advantages of linear models as well as the flexibility of deep neural networks. We show that our model predicts realistic daily winter precipitation fields at high resolution based on a 50-member ensemble of the Canadian Regional Climate Model at 12 km resolution over Europe, capturing, for instance, key orographic features and geographical gradients. Using the Latent Linear Adjustment Autoencoder to remove the dynamic component of precipitation variability, forced thermodynamic components are expected to remain in the residual, which enables the uncovering of forced precipitation patterns of change from just a few ensemble members. We extend this to quantify the forced pattern of change conditional on specific circulation regimes. Future applications could include, for instance, weather generators emulating climate model simulations of regional precipitation, detection and attribution at subcontinental scales, or statistical downscaling and transfer learning between models and observations to exploit the typically much larger sample size in models compared to observations. [ABSTRACT FROM AUTHOR]

Studies of emergent constraints have frequently proposed that a single metric can constrain future responses of the Earth system to anthropogenic emissions. Here, we illustrate that strong relationships between observables and future climate across an ensemble can arise from common structural model assumptions with few degrees of freedom. Such cases have the potential to produce strong yet overconfident constraints when processes are represented in a common, oversimplified fashion throughout the ensemble. We consider these issues in the context of a collection of published constraints and argue that although emergent constraints are potentially powerful tools for understanding ensemble response variation and relevant observables, their naïve application to reduce uncertainties in unknown climate responses could lead to bias and overconfidence in constrained projections. The prevalence of this thinking has led to literature in which statements are made on the probability bounds of key climate variables that were confident yet inconsistent between studies. Together with statistical robustness and a mechanism, assessments of climate responses must include multiple lines of evidence to identify biases that can arise from shared, oversimplified modelling assumptions that impact both present and future climate simulations in order to mitigate against the influence of shared structural biases. [ABSTRACT FROM AUTHOR]

A key challenge in climate science is to quantify the forced response in impact-relevant variables such as precipitation against the background of internal variability, both in models and observations. Dynamical adjustment techniques aim to remove unforced variability from a target variable by identifying patterns associated with circulation, thus effectively acting as a filter for dynamically-induced variability. The forced contributions are interpreted as the variation that is unexplained by circulation. However, dynamical adjustment of precipitation at local scales remains challenging because of large natural variability and the complex, nonlinear relationship between precipitation and circulation particularly in heterogeneous terrain. Building on variational autoencoders, we introduce a novel statistical model—the Latent Linear Adjustment Autoencoder—that enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high-resolution and in a spatially coherent manner. To predict circulation-induced precipitation, the Latent Linear Adjustment Autoencoder combines a linear component, which models the relationship between circulation and the latent space of an autocoder, with the autoencoder's nonlinear decoder. The combination is achieved by imposing an additional penalty in the cost function that encourages linearity between the circulation field and the autoencoder's latent space, hence leveraging robustness advantages of linear models as well as the flexibility of deep neural networks. We show that our model predicts realistic daily winter precipitation fields at high resolution based on a 50-member ensemble of the Canadian Regional Climate Model at 12-km resolution over Europe, capturing for instance key orographic features and geographical gradients. Using the Latent Linear Adjustment Autoencoder to remove the dynamic component of precipitation variability, forced thermodynamic components are expected to remains in the residual, which enables the uncovering of forced precipitation patterns of change from just a few ensemble members. We extend this to quantify the forced pattern of change conditional on specific circulation regimes. In addition, we briefly illustrate one of multiple possible further applications of the method: a weather generator that emulates climate model simulations of regional precipitation at high resolution by bootstrapping circulation patterns. Other potential applications include addressing detection&attribution at sub-continental scales, statistical downscaling and transfer learning between models and observations to exploit the typically much larger sample size in models compared to observations. [ABSTRACT FROM AUTHOR]

The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the models' historical performance based on several diagnostics as well as model interdependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 "family tree", which enables the application of a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (the fifth generation of the European Centre for Medium-Range Weather Forecasts Retrospective Analysis – ERA5, and the Modern-Era Retrospective analysis for Research and Applications, version 2 – MERRA-2), to constrain CMIP6 projections under weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios (SSP refers to the Shared Socioeconomic Pathways). Our results show a reduction in the projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 ∘ C, compared with 4.1 ∘ C without weighting; the likely (66%) uncertainty range is 3.1 to 4.6 ∘ C, which equates to a 13 % decrease in spread. For SSP1-2.6, the weighted end-of-century warming is 1 ∘ C (0.7 to 1.4 ∘ C), which results in a reduction of -0.1 ∘ C in the mean and -24 % in the likely range compared with the unweighted case. [ABSTRACT FROM AUTHOR]

Climate change intensifies the Earth's hydrologic cycle, which has far‐reaching consequences including water availability, agricultural production, and electric power generation. The rate of intensification projected by state‐of‐the‐art global climate models (GCMs) with increasing greenhouse gas emissions, however, is highly uncertain. Thackeray et al. (2018, https://doi.org/10.1029/2018GL079698) show that these uncertainties are related to how GCMs distribute future precipitation by either strongly increasing extreme precipitation at the cost of nonextreme events or by increasing nonextreme precipitation at the cost of extreme precipitation events. These results could help to constrain uncertainties in future hydrologic cycle intensification, thereby improving our understanding of future water resource availability and extreme hydrologic events. Plain Language Summary: How precipitation changes with warming is critically important for anticipating and responding to climate change, but climate models still disagree on many aspects of its change. A new study by Thackeray et al. (2018, https://doi.org/10.1029/2018GL079698) reveals two intriguing aspects of precipitation change among the most recent generation of climate model projections: a compensation between precipitation change arising from the heaviest events and events that are not heavy events, and a dependence of this relationship on the climate model's resolution. We argue that their findings point toward paths that could lead to further understanding and improvements in simulations of precipitation change. Key Points: New findings by Thackeray et al. (2018) show compensating effects between modeled climate changes in extreme and nonextreme precipitationThese findings might help to constrain climate change projections of the Earth's hydrologic cycle [ABSTRACT FROM AUTHOR]

Abstract: We compare various radiative feedbacks over the Arctic (60–90°N) estimated from short‐term climate variations occurring in reanalysis, satellite, and global climate model data sets using the combined Kernel‐Gregory approach. The lapse rate and surface albedo feedbacks are positive, and their magnitudes are comparable. Relative to the tropics (30°S–30°N), the lapse rate feedback is the largest contributor to Arctic amplification among all feedbacks, followed by surface albedo feedback and Planck feedback deviation from its global mean. Both shortwave and longwave water vapor feedbacks are positive, leading to a significant positive net water vapor feedback over the Arctic. The net cloud feedback has large uncertainties including its sign, which strongly depends on the data used for all‐sky and clear‐sky radiative fluxes at the top of the atmosphere, the time periods considered, and the methods used to estimate the cloud feedback. [ABSTRACT FROM AUTHOR]

Radiative kernels at the top of atmosphere are useful for decomposing changes in atmospheric radiative fluxes due to feedbacks from atmosphere and surface temperature, water vapor, and surface albedo. Here we describe and validate radiative kernels calculated with the large-ensemble version of CAM5, CESM 1.1.2, at the top-of-atmosphere and the surface. Estimates of the radiative forcing from greenhouse gases and aerosols in RCP8.5 in the CESM large ensemble simulations are also included. As an application, feedbacks are calculated for the CESM large ensemble. The kernels are freely available at https://zenodo.org/record/997902, and accompanying software can be downloaded from https://github.com/apendergrass/cam5-kernels. [ABSTRACT FROM AUTHOR]