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Interannual fluctuations in average summertime temperatures across the western United States are captured by a leading empirical orthogonal function that explains over 50% of the total observed variance. In this paper, we explain the origins of this pattern of interannual temperature variability by examining soil moisture–temperature coupling that acts across seasons in observations and climate models. We find that a characteristic pattern of coupled temperature–soil moisture climate variability accounts for 34% of the total observed variance in summertime temperature across the region. This pattern is reproduced in state-of-the-art global climate models, where experiments that eliminate soil moisture variability reduce summertime average temperature variance by a factor of 3 on average. We use an idealized model of the coupled atmospheric boundary layer and underlying land surface to demonstrate that feedbacks between soil moisture, boundary layer relative humidity, and precipitation can explain the observed relations between springtime soil moisture and summertime temperature. Our results suggest that antecedent soil moisture conditions and subsequent land–atmosphere interactions play an important role in interannual summertime temperature variability in the western United States; soil moisture variations cause distal temperature anomalies and impart predictability at time scales longer than one season. Our results indicate that 40% of the observed warming trend across the western United States since 1981 has been driven by wintertime precipitation trends in the U.S. southwest. Significance Statement: Year-to-year fluctuations in summertime temperatures have a large impact on drought, wildfire, and extreme heat across the Western United States. We find strong evidence that soil moisture deficits in the preceding spring may be the primary driver of higher-than-average summer temperatures in this region. Our results suggest that memory in the water cycle may lead to greater predictability in the climate system from season to season and that trends in southwest precipitation have exerted a considerable influence on observed warming over the past 40 years. [ABSTRACT FROM AUTHOR]

This paper presents a methodology that is designed for rapid exploratory analysis of the outputs from ensembles of climate models, especially when these outputs consist of maps. The approach formalizes and extends the technique of "intermodel empirical orthogonal function" analysis, combining multivariate analysis of variance techniques with singular value decompositions (SVDs) of structured components of the ensemble data matrix. The SVDs yield spatial patterns associated with these components, which we call ensemble principal patterns (EPPs). A unique hierarchical partitioning of variation is obtained for balanced ensembles in which all combinations of factors, such as GCM and RCM pairs in a regional ensemble, appear with equal frequency: suggestions are also proposed to handle unbalanced ensembles without imputing missing values or discarding runs. Applications include the selection of ensemble members to propagate uncertainty into subsequent analyses, and the diagnosis of modes of variation associated with specific model variants or parameter perturbations. The approach is illustrated using outputs from the EuroCORDEX regional ensemble over the United Kingdom. [ABSTRACT FROM AUTHOR]

This paper examines the trends in hot summer days for the Pacific Northwest in observations and a regional climate model ensemble. Hot days are identified by the temperature threshold for several percentile values computed over 10-yr intervals (85th, 90th, and 95th percentiles and absolute maximum) to differentiate heat events of different intensities and are compared to the median temperature (50th percentile). For the stations analyzed, the observed rate of warming during hot days is not statistically different from the warming rate of median days since the 1950s. However, for projections to 2100, hot days show a statistically significant increase in the warming rate of the hottest days compared to the warming rate for median days. Depending on location, the 95th-percentile daily maximum temperature shows a warming rate of up to 0.2°C decade−1 above the median warming rate. The divergence in the trends of median and extreme temperature shows substantial regional variation depending on local terrain and coastlines. The warming trend during hot days is related to the unique circulation patterns during heat events, which respond to different feedbacks and amplifying effects in the land–atmosphere system from those that prevail during typical days. The regional climate model simulations are taken from an ensemble of the Weather Research and Forecasting (WRF) Model simulations forced by 12 global climate model simulations from phase 5 of the Climate Model Intercomparison Project (CMIP5) using the RCP8.5 emissions scenario and 12-km grid spacing. Significance Statement: The heatwave of June 2021 had substantial societal and ecological impacts, illustrating vulnerability to record-shattering events. This paper addresses whether climate change can cause heat events in the Pacific Northwest to warm at a more rapid rate than typical days. Based on observations, in the recent past, the hottest days warmed at about the same rate as typical days in the recent past. However, results from a high-resolution climate model show a higher rate of warming during the warmest days relative to median days under the most aggressive future emissions scenario. This effect could cause heatwaves to become more intense relative to typical days by 2100, making adaptation to extreme events more difficult. [ABSTRACT FROM AUTHOR]

Snow cover (SC) is an important contributor to atmospheric predictability on subseasonal to seasonal time scales. This paper evaluates the submonthly scale cause-and-effect relationship between SC and surface air temperature (SAT) in Eurasia, which has been typically overlooked in previous statistical analyses of subseasonal to seasonal atmospheric predictability. We focus on the November east–west dipolar SC pattern, a dominant large-scale SC phenomenon. We use an information flow analysis, based on information theory, to infer causal relations not revealed by conventional correlation analysis. This analysis indicates a one-way causality from SAT to SC around the west pole (Europe) in boreal autumn (November), implying that SC has little influence on the time evolution of SAT. In contrast, causality from SC to SAT is significant around the east pole (the Mongolian Plateau). An atmospheric model experiment suggests that the SAT response to SC can persist for a month via snow–albedo feedback, although the response of the upper atmosphere in the model is small. Furthermore, the subseasonal hindcasts show that the contribution of SC may affect the predictability of SAT for up to four weeks around the east pole. We suggest that the geographical and climatological atmospheric conditions are favorable for generating a positive albedo feedback as a “hotspot” of SC–SAT coupling around the east pole in autumn. The agreement of our causality analysis with other analytical and modeling approaches underscores the cause-and-effect relationship between SC and SAT and its contribution to the subseasonal predictability over autumnal Eurasia. [ABSTRACT FROM AUTHOR]

A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

This paper describes the downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement: This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation. [ABSTRACT FROM AUTHOR]

An interesting aspect of the El Ni˜no-Southern Oscillation (ENSO) phenomenon is the asymmetry between its two phases. This paper evaluates the simulations of this property of ENSO by the CoupledModel Intercomparison Project phase 6 (CMIP6) models. Both the surface and subsurface signals of ENSO are examined for this purpose. The results show that the models still underestimate ENSO asymmetry as shown in the SST field, but do a better job in the subsurface. A much weaker negative feedback from the net surface heat flux during La Ni˜na in the models is identified as a factor causing the degradation of the ENSO asymmetry at the surface. The simulated asymmetry in the subsurface is still weaker than the observations owing to a weaker dynamic coupling between the atmosphere and ocean. Consistent with the finding of a weaker dynamic coupling strength, the precipitation response to the SST changes is also found to be weaker in the models. The results underscore that a more objective assessment of the simulation of ENSO by climate models may have to involve the examination of the subsurface signals. Future improvements in simulating ENSO will likely require a better simulation of the surface heat flux feedback from the atmosphere as well as the dynamical coupling strength between the atmosphere and ocean. [ABSTRACT FROM AUTHOR]

Global climate models, like any in silico numerical experiments, are affected by different types of bias. Uncertainty quantification remains a challenge in any climate detection and attribution analysis. A fundamental methodological question is to determine which statistical summaries, while bringing relevant signals, can be robust with respect to multimodel errors. In this paper, we propose a simple statistical framework that significantly improves signal detection in climate attribution studies. We show that the complex bias correction step can be entirely bypassed for models for which bias between the simulated and unobserved counterfactual worlds is the same as between the simulated and unobserved factual worlds. To illustrate our approach, we infer emergence times in precipitation from the CMIP5 and CMIP6 archives. The detected anthropogenic signal in yearly maxima of daily precipitation clearly emerges at the beginning of the twenty-first century. In addition, no CMIP model seems to outperform the others and a weighted linear combination of all improves the estimation of emergence times. Significance Statement: We show that the bias in multimodel global climate simulations can be efficiently handled when the appropriate metric is chosen. This metric leads to an easy-to-implement statistical procedure based on a checkable assumption. This allows us to demonstrate that optimal convex combinations of CMIP outputs can improve the signal strength in finding emergence times. Our data analysis procedure is applied to yearly maximum of precipitation from CMIP5 and CMIP6 databases. The attribution of the anthropogenic forcing clearly emerges in extreme precipitation at the beginning of the twenty-first century. [ABSTRACT FROM AUTHOR]

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development. [ABSTRACT FROM AUTHOR]

In a recent paper, we argued that ocean dynamics increase the variability of midlatitude sea surface temperatures (SSTs) on monthly to interannual time scales, but act to damp lower-frequency SST variability over broad midlatitude regions. Here, we use two configurations of a simple stochastic climate model to provide new insights into this important aspect of climate variability. The simplest configuration includes the forcing and damping of SST variability by observed surface heat fluxes only, and the more complex configuration includes forcing and damping by ocean processes, which are estimated indirectly from monthly observations. It is found that the simple model driven only by the observed surface heat fluxes generally produces midlatitude SST power spectra that are too red compared to observations. Including ocean processes in the model reduces this discrepancy by whitening the midlatitude SST spectra. In particular, ocean processes generally increase the SST variance on <2-yr time scales and decrease it on >2-yr time scales. This happens because oceanic forcing increases the midlatitude SST variance across many time scales, but oceanic damping outweighs oceanic forcing on >2-yr time scales, particularly away from the western boundary currents. The whitening of midlatitude SST variability by ocean processes also operates in NCAR's Community Earth System Model (CESM). That is, midlatitude SST spectra are generally redder when the same atmospheric model is coupled to a slab rather than dynamically active ocean model. Overall, the results suggest that forcing and damping by ocean processes play essential roles in driving midlatitude SST variability. [ABSTRACT FROM AUTHOR]

Evidence from previous climate model simulations has suggested a potentially low efficacy of contrails to force global mean surface temperature changes. In this paper, a climate model with a state-of-the-art contrail cirrus representation is used for fixed sea surface temperature simulations in order to determine the effective radiative forcing (ERF) from contrail cirrus. ERF is expected to be a good metric for intercomparing the quantitative importance of different contributions to surface temperature and climate impact. Substantial upscaling of aviation density is necessary to ensure statistically significant results from our simulations. The contrail cirrus ERF is found to be less than 50% of the respective instantaneous or stratosphere adjusted radiative forcings, with a best estimate of roughly 35%. The reduction of ERF is much more substantial for contrail cirrus than it is for a CO2 increase when both stratosphere adjusted forcings are of similar magnitude. Analysis of all rapid radiative adjustments contributing to the ERF indicates that the reduction is mainly induced by a compensating effect of natural clouds that provide a negative feedback. Compared to the CO2 reference case, a less positive combined water vapor and lapse rate adjustment also contributes to a more distinct reduction of contrail cirrus ERF, but not as much as the natural cloud adjustment. Based on the experience gained in this paper, respective contrail cirrus simulations with interactive ocean will be performed as the next step toward establishing contrail cirrus efficacy. ERF results of contrail cirrus from other climate models equipped with suitable parameterizations are regarded as highly desirable. [ABSTRACT FROM AUTHOR]

A key question in climate modeling is to what extent sea surface temperature and upper-ocean heat content are driven passively by air-sea heat fluxes, as opposed to forcing by ocean dynamics. This paper investigates the question using a climate model at different resolutions, and observations, for monthly variability. At the grid scale in a high-resolution climate model with resolved mesoscale ocean eddies, ocean dynamics (i.e., ocean heat flux convergence) dominates upper 50 m heat content variability over most of the globe. For deeper depths of integration to 400 m, the heat content variability at the grid scale is almost totally controlled by ocean heat flux convergence. However, a strong dependence on spatial scale is found--for the upper 50 m of ocean, after smoothing the data to around 7°, air-sea heat fluxes, augmented by Ekman heat transports, dominate. For deeper depths of integration to 400 m, the transition scale becomes larger and is above 10° in western boundary currents. Comparison of climate model results with observations show that the small-scale influence of ocean intrinsic variability is well captured by the high-resolution model but is missing from a comparable model with parameterized ocean-eddy effects. In the deep tropics, ocean dynamics dominates in all cases and all scales. In the subtropical gyres at large scales, air-sea heat fluxes play the biggest role. In the midlatitudes, at large scales >10°, atmosphere-driven air-sea heat fluxes and Ekman heat transport variability are the dominant processes except in the western boundary currents for the 400 m heat content. [ABSTRACT FROM AUTHOR]

The literature offers conflicting findings about which regions contribute most to increases in the global radiative feedback after a forcing increase. This paper explains the disagreement by discriminating between two common definitions of the local feedback, which use either local temperature or global temperature as their basis. Although the two definitions of feedback have been previously compared in aquaplanet models with slab oceans, here the definitions are compared for the first time in an atmosphere-ocean general circulation model (MPI-ESM1.2) integrated over four doublings of atmospheric CO2 concentrations. Large differences between the definitions can be seen in all feedbacks, but especially in the temperature and water vapor feedbacks. Differences of up to 10 W m-2 K-1 over the Southern Ocean can be explained by the pattern of surface warming, which weights the local feedbacks and reduces their contribution to the global mean. This finding is, however, dependent on the resolution of analysis, because the local-temperature definition is mathematically inconsistent across spatial scales. Furthermore, attempts to estimate the effect of "pattern weighting" by separating local feedbacks and warming patterns at the gridcell level fail, because the radiative change in key tropical regions is also determined by tropospheric stability via the global circulation. These findings indicate that studies of regional feedback change are more sensitive to methodological choices than previously thought, and that the tropics most likely dominate regional contributions to global radiative feedback change on decadal to centennial time scales. [ABSTRACT FROM AUTHOR]

Climate models project an intensification of the wintertime North Atlantic Ocean storm track, over its downstream region, by the end of this century. Previous studies have suggested that ocean–atmosphere coupling plays a key role in this intensification, but the precise role of the different components of the coupling has not been explored and quantified. In this paper, using a hierarchy of ocean coupling experiments, we isolate and quantify the respective roles of thermodynamic (changes in surface heat fluxes) and dynamic (changes in ocean heat flux convergence) ocean coupling in the projected intensification of North Atlantic transient eddy kinetic energy (TEKE). We show that dynamic coupling accounts for nearly all of the future TEKE strengthening as it overcomes the much smaller effect of surface heat flux changes to weaken the TEKE. We further show that by reducing the Arctic amplification in the North Atlantic, ocean heat flux convergence increases the meridional temperature gradient aloft, causing a larger eddy growth rate and resulting in the strengthening of North Atlantic TEKE. Our results stress the importance of better monitoring and investigating the changes in ocean heat transport, for improving climate change adaptation strategies. Significance Statement: By the end of this century, the North Atlantic Ocean storm track is projected to intensify on its eastward flank. Such intensification will have large societal impacts, mostly over western Europe. Thus, it is critical to better understand the mechanism underlying the intensification of the storm track. Here we investigate the role of ocean coupling in the future intensification of the North Atlantic storm track and find that ocean heat transport processes are responsible for the strengthening of the storm track. Our results suggest that better monitoring the changes in ocean heat transport will hopefully improve climate change adaption strategies. [ABSTRACT FROM AUTHOR]

Realistic ocean subsurface simulations of thermal structure and variation are critically important to success in climate prediction and projection; currently, substantial systematic subsurface biases still exist in the state-of-the-art ocean and climate models. In this paper, subsurface biases in the tropical Atlantic Ocean (TA) are investigated by analyzing simulations from the Ocean Model Intercomparison Project (OMIP) and conducting ocean-only experiments that are based on the Parallel Ocean Program, version 2 (POP2). The subsurface biases are prominent in almost all OMIP simulations, characterized by two warm-bias patches off the equator. By conducting two groups of POP2-based ocean-only experiments, two potential origins of the biases are explored, including uncertainties in wind forcing and vertical mixing parameterization, respectively. It is illustrated that the warm bias near 10°N can be slightly reduced by modulating the prescribed wind field, and the warm biases over the entire basin are significantly reduced by reducing background diffusivity in the ocean interior in ways to match observations. By conducting a heat-budget analysis, it is found that the improved subsurface simulations are attributed to the enhanced cooling effect by constraining the vertical mixing diffusivity in terms of the observational estimate, implying that overestimation of vertical mixing is primarily responsible for the subsurface warm biases in the TA. Since the climate simulation is very sensitive to the vertical mixing parameterization, more accurate representations of ocean vertical mixing are clearly needed in ocean and climate models. Significance Statement: The purpose of our study is to analyze the characteristics of subsurface temperature biases in the tropical Atlantic Ocean and to investigate the causes for the biases. This is important because subsurface biases greatly reduce the reliability of models in climate prediction and projection. It is found that significant subsurface warm biases arise in 100–150 m over the entire tropical Atlantic basin and the biases are mainly attributed to overestimated ocean vertical mixing. Our work highlights that subsurface ocean simulations are highly sensitive to vertical mixing parameterization, and further research is necessary for more accurate representations of ocean vertical mixing in ocean and climate modeling. [ABSTRACT FROM AUTHOR]

Interdecadal variations of the relationship between El Niño–Southern Oscillation (ENSO) and the Indo-China Peninsula (ICP) surface air temperature (SAT) in winter are investigated in the study. Generally, there exists a positive correlation between them during 1958–2015 because the ENSO-induced anomalous western North Pacific anticyclone (WNPAC) is conducive to pronounced temperature advection anomalies over the ICP. However, such correlation is unstable in time, having experienced a high-to-low transition around the mid-1970s and a recovery since the early 1990s. This oscillating relationship is owing to the anomalous WNPAC intensity in different decades. During the epoch of high correlation, the anomalous WNPAC and associated southwesterly winds over the ICP are stronger, which brings amounts of warm temperature advection and markedly heats the ICP. In contrast, a weaker WNPAC anomaly and insignificant ICP SAT anomalies are the circumstances for the epoch of low correlation. It is also found that substantial southwesterly wind anomalies over the ICP related to the anomalous WNPAC occur only when large sea surface temperature (SST) anomalies over the northwest Indian Ocean (NWIO) coincide with ENSO (viz., when the ENSO–NWIO SST connection is strong). The NWIO SST anomalies are capable of driving favorable atmospheric circulation that effectively alters ICP SAT and efficiently modulates the ENSO–ICP SAT correlation, which is further supported by numerical simulations utilizing the Community Atmospheric Model, version 4 (CAM4). This paper emphasizes the non-stationarity of the ENSO–ICP SAT relationship and also uncovers the underlying modulation factors, which has important implications for the seasonal prediction of the ICP temperature. [ABSTRACT FROM AUTHOR]

Global warming projections of dynamics are less robust than projections of thermodynamics. However, robust aspects of the thermodynamics can be used to constrain some dynamical aspects. This paper argues that tropospheric expansion under global warming (a thermodynamical process) explains changes in the amplitude of the quasi-biennial oscillation (QBO) in the lower and middle stratosphere (a dynamical process). A theoretical scaling for tropospheric expansion of approximately 6 hPa K−1 is derived, which agrees well with global climate model (GCM) experiments. Using this theoretical scaling, the response of QBO amplitude to global warming is predicted by shifting the climatological QBO amplitude profile upward by 6 hPa per kelvin of global warming. In global warming simulations, QBO amplitude in the lower to middle stratosphere shifts upward as predicted by tropospheric expansion. Applied to observations, the tropospheric expansion framework suggests a historical weakening of QBO amplitude at 70 hPa of 3% decade−1 from 1953 to 2020. This expected weakening trend is half of the 6% decade−1 from 1953 to 2012 detected and attributed to global warming in a recent study. The previously reported trend was reinforced by record low QBO amplitudes during the mid-2000s, from which the QBO has since recovered. Given the modest weakening expected on physical grounds, past decadal modulations of QBO amplitude are reinterpreted as a hitherto unrecognized source of internal variability. This large internal variability dominates over the global warming signal such that, despite 65 years of observations, there is not yet a statistically significant weakening trend. [ABSTRACT FROM AUTHOR]

A traditional view is that the ocean outside of the tropics responds passively to atmosphere forcing, which implies that air–sea heat fluxes are mainly driven by atmosphere variability. This paper tests this viewpoint using state-of-the-art air–sea turbulent heat flux observational analyses and a climate model run at different resolutions. It is found that in midlatitude ocean frontal zones the variability of air–sea heat fluxes is not predominantly driven by the atmosphere variations but instead is forced by sea surface temperature (SST) variations arising from intrinsic oceanic variability. Meanwhile in most of the tropics and subtropics wind is the dominant driver of heat flux variability, and atmosphere humidity is mainly important in higher latitudes. The predominance of ocean forcing of heat fluxes found in frontal regions occurs on scales of around 700 km or less. Spatially smoothing the data to larger scales results in the traditional atmosphere-driving case, while filtering to retain only small scales of 5° or less leads to ocean forcing of heat fluxes over most of the globe. All observational analyses examined (1° OAFlux; 0.25° J-OFURO3; 0.25° SeaFlux) show this general behavior. A standard resolution (1°) climate model fails to reproduce the midlatitude, small-scale ocean forcing of heat flux: refining the ocean grid to resolve eddies (0.1°) gives a more realistic representation of ocean forcing but the variability of both SST and of heat flux is too high compared to observational analyses. [ABSTRACT FROM AUTHOR]

Previous studies have recognized the societal relevance of climatic extremes on the seasonal time scale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet orwarm seasons). However, these findings have not yet been generalized beyond case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogs to even themost extreme ERA-Interimevents. This approach offers numerous opportunities for systematically analyzing large samples of extreme seasons, and several such analyses are illustrated. We reveal a striking co-occurrence of ElNiño to La Niña transitions and the largest ERA-Interim midlatitude extreme summer events. Moreover, we perform a climate model evaluation with regard to extreme season size and intensitymeasures and estimate how often an extreme winter like the coldNorth American 2013/14 winter is expected anywhere inmidlatitude regions. Furthermore, we present a large set of simulated analogs to this event, whichmakes it possible to studycommonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified. [ABSTRACT FROM AUTHOR]

The impact of two different coupled cirrus microphysics-radiation parameterizations on the zonally averaged temperature and humidity biases in the tropical tropopause layer (TTL) of a Met Office climate model configuration is assessed. One parameterization is based on a linear coupling between a model prognostic variable, the ice mass mixing ratio q i, and the integral optical properties. The second is based on the integral optical properties being parameterized as functions of q i and temperature, T c, where the mass coefficients (i.e., scattering and extinction) are parameterized as nonlinear functions of the ratio between q i and T c. The cirrus microphysics parameterization is based on a moment estimation parameterization of the particle size distribution (PSD), which relates the mass moment (i.e., second moment if mass is proportional to size raised to the power of 2) of the PSD to all other PSD moments through the magnitude of the second moment and T c. This same microphysics PSD parameterization is applied to calculate the integral optical properties used in both radiation parameterizations and, thus, ensures PSD and mass consistency between the cirrus microphysics and radiation schemes. In this paper, the temperature-non-dependent and temperature-dependent parameterizations are shown to increase and decrease the zonally averaged temperature biases in the TTL by about 1 K, respectively. The temperature-dependent radiation parameterization is further demonstrated to have a positive impact on the specific humidity biases in the TTL, as well as decreasing the shortwave and longwave biases in the cloudy radiative effect. The temperature-dependent radiation parameterization is shown to be more consistent with TTL and global radiation observations. [ABSTRACT FROM AUTHOR]

This paper shows that joint temperature-precipitation information over a global domain provides a more accurate estimate of aerosol forced responses in climate models than does any other combination of temperature, precipitation, or sea level pressure. This fact is demonstrated using a new quantity called potential detectability, which measures the extent to which a forced response can be detected in a model. In particular, this measure can be evaluated independently of observations and therefore permits efficient exploration of a large number of variable combinations before performing optimal fingerprinting on observations. This paper also shows that the response to anthropogenic aerosol forcing can be separated from that of other forcings using only spatial structure alone, leaving the time variation of the response to be inferred from data, thereby demonstrating that temporal information is not necessary for detection. The spatial structure of the forced response is derived by maximizing the signal-to-noise ratio. For single variables, the north-south hemispheric gradient and equator-to-pole latitudinal gradient are important spatial structures for detecting anthropogenic aerosols in some models but not all. Sea level pressure is not an independent detection variable because it is derived partly from surface temperature. In no case does sea level pressure significantly enhance potential detectability beyond that already possible using surface temperature. Including seasonal or land-sea contrast information does not significantly enhance detectability of anthropogenic aerosol responses relative to annual means over global domains. [ABSTRACT FROM AUTHOR]

This paper attempts to enhance our understanding of the causes of Atlantic multidecadal variability (AMV). Following the literature, we define the AMV as the SST averaged over the North Atlantic basin, linearly detrended and low-pass filtered. There is an ongoing debate about the drivers of the AMV, which include internal variability generated from the ocean or atmosphere (or both) and external radiative forcing. We test the role of these factors in explaining the time history, variance, and spatial pattern of the AMV using a 41-member ensemble from a fully coupled version of CESM and a 10-member ensemble of the CESM atmosphere coupled to a slab ocean. The large ensemble allows us to isolate the role of external forcing versus internal variability, and the model differences allow us to isolate the role of coupled ocean circulation. Both with and without coupled ocean circulation, external forcing explains more than half of the variance of the observed AMV time series, indicating its important role in simulating the twentieth-century AMV phases. In this model the net effect of ocean processes is to reduce the variance of the AMV. Dynamical ocean coupling also reduces the ability of the model to simulate the characteristic spatial pattern of the AMV, but forcing has little impact on the pattern. Historical forcing improves the time history and variance of the AMV simulation, while the more realistic ocean representation reduces the variance below that observed and lowers the correlation with observations. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation (ENSO) diversity has a significant impact on global climate and seasonal prediction. However, it is still a challenging problem for present-day global climate models to simulate different types of ENSO events with realistic features simultaneously. In this paper, a tropical stochastic skeleton model for the interactions among wind bursts and the Madden–Julian oscillation (MJO), the El Niño, and the Walker circulation is developed to reproduce both dynamical and statistical features of the ENSO diversity. In this model, the intraseasonal component with state-dependent noise captures general features of wind bursts and the MJO, both of which play important roles in triggering the El Niño. The thermocline feedback is the dominant mechanism for generating the eastern Pacific (EP) El Niño, while a nonlinear zonal advection is incorporated into the model that contributes to the central Pacific (CP) El Niño. Besides, a simple but effective stochastic process describing the multidecadal variation of the background Walker circulation modulates the spatial patterns and occurrence frequency of the EP and CP El Niño. This model succeeds in simulating the quasi-regular moderate EP El Niño, the super El Niño, and the CP El Niño as well as the La Niña simultaneously. It also captures the observed non-Gaussian characteristics of sea surface temperature anomalies in different Niño regions. Individual case studies highlight the outstanding skill of the model in reproducing the observed El Niño episodes and their underlying mechanisms. [ABSTRACT FROM AUTHOR]

Global positioning system (GPS) radio occultation (RO) observations, first made of Earth's atmosphere in 1995, have contributed in new ways to the understanding of the thermal structure and variability of the tropical upper troposphere–lower stratosphere (UTLS), an important component of the climate system. The UTLS plays an essential role in the global radiative balance, the exchange of water vapor, ozone, and other chemical constituents between the troposphere and stratosphere, and the transfer of energy from the troposphere to the stratosphere. With their high accuracy, precision, vertical resolution, and global coverage, RO observations are uniquely suited for studying the UTLS and a broad range of equatorial waves, including gravity waves, Kelvin waves, Rossby and mixed Rossby–gravity waves, and thermal tides. Because RO measurements are nearly unaffected by clouds, they also resolve the upper-level thermal structure of deep convection and tropical cyclones as well as volcanic clouds. Their low biases and stability from mission to mission make RO observations powerful tools for studying climate variability and trends, including the annual cycle and intraseasonal-to-interannual atmospheric modes of variability such as the quasi-biennial oscillation (QBO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO). These properties also make them useful for evaluating climate models and detection of small trends in the UTLS temperature, key indicators of climate change. This paper reviews the contributions of RO observations to the understanding of the three-dimensional structure of tropical UTLS phenomena and their variability over time scales ranging from hours to decades and longer. [ABSTRACT FROM AUTHOR]

When record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of reoccurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized subseasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled subseasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere–ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period (i.e., before they occur). This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services. [ABSTRACT FROM AUTHOR]

The Earth's ice-ocean-atmosphere system exhibits nonlinear responses, such as the difference in the magnitude of the atmospheric response to positive or negative ocean or sea ice anomalies. Two classes of methods that have previously been used to investigate the nonlinear dependence between climate fields are kernel methods and neural network methods. In this paper, a third methodology is introduced: gradient-based kernel dimension reduction. Gradient-based kernel methods are an extension of conventional kernel methods, but gradient-based methods focus on the directional derivatives of the regression surface between two fields. Specifically, a new gradient-based method is developed here: gradient kernel canonical correlation analysis (gKCCA). In gKCCA, the canonical directions maximize the directional derivatives between the predictor field and the response field, while the canonical components of the response field maximize the correlation with a nonlinear augmentation of the predictor canonical components. Gradient-based kernel methods have several advantages: their components can be directly related to the original fields (unlike in conventional kernel methods), and the projection vectors are determined by analytical solution (unlike in neural networks). Here gKCCA is applied to the question of nonlinear coupling between high-frequency (2-3 years) and low-frequency (4-6 years) modes in the Pacific Ocean. The leading gKCCA subspace shows a significant nonlinear coupling between the low-pass and high-pass fields. The paper also shows that the results of gKCCA are robust to different levels of noise and different kernel functions. [ABSTRACT FROM AUTHOR]

The paths of winter cold surge (CS) events in East Asia (EA) from 1979 to 2017 are tracked by the Flexible Particle (FLEXPART) model using ERA-Interim daily datasets, and the probability density distribution of the paths is calculated by the kernel density estimation (KDE) method. The results showed that the paths of CSs are significantly correlated with the intensity of the CSs, which shows an interdecadal transition from weak to strong around 1995. CS paths can be classified into two types, namely, the western path type and the northern path type, which were more likely to occur before and after 1995, respectively. Before 1995, the cold air mainly originated from Europe and moved from west to east, and the synoptic features were associated with the zonal wave train. After 1995, cold air accumulated over western Siberia and then invaded EA along the northern path, and the synoptic features were mainly associated with the blocking structure. The geopotential height (GPH) anomalies over the Arctic were abnormally strong. This paper further analyzes the relationship between CSs and winter sea ice concentration (SIC) in the Arctic. The results show that the intensity of CSs is negatively correlated with the Barents SIC (BSIC). When the BSIC declines, the upward wave flux over the Barents Sea is enhanced and expanded to the midlatitude region. GPH anomalies over the Arctic are positive and form a negative AO-like pattern, which is conducive to the formation of the northern path CS. Furthermore, the observed results are supported by numerical experiments with the NCAR Community Atmosphere Model, version 5.3 (CAM5.3). [ABSTRACT FROM AUTHOR]

Climate model comparisons show that there is considerable uncertainty in the atmospheric temperature response to CO2 perturbation. The uncertainty results from both the rapid adjustment that occurs before SST changes and the slow feedbacks that occur after SST changes. The analysis in this paper focuses on the rapid adjustment.We use a novel method to decompose the temperature change in AMIP-type climate simulation in order to understand the adjustment at the process level. We isolate the effects of different processes, including radiation, convection, and large-scale circulation in the temperature adjustment, through a set of numerical experiments using a hierarchy of climate models. We find that radiative adjustment triggers and largely controls the zonal mean atmospheric temperature response pattern. This pattern is characterized by stratospheric cooling, lower-tropospheric warming, and a warming center near the tropical tropopause. In contrast to conventional views, the warming center near the tropopause is found to be critically dependent on the shortwave absorption of CO2. The dynamical processes largely counteract the effect of the radiative process that increases the vertical temperature gradient in the free troposphere. The effect of local convection is to move atmospheric energy vertically, which cools the lower troposphere and warms the upper troposphere. The adjustment due to large-scale circulation further redistributes energy along the isentropic surfaces across the latitudes, which cools the low-latitude lower troposphere and warms the midlatitude upper troposphere and stratosphere. Our results highlight the importance of the radiative adjustment in the overall adjustment and provide a potential method to understand the spread in the models. [ABSTRACT FROM AUTHOR]

This paper studies regional climate variability for the time period 1900-2013 using parsimonious stochastic models. Instrumental data records on 5° × 5°, 2° × 2°, and equal-area grids are examined. A long-range dependent (LRD) stochastic process is used as a simplified description of the multitude of response times in the climate system. Fitting a linear trend to the global mean surface temperature (GMST) implies a warming of 0.08 decade−1, which is highly significant under an LRD null hypothesis ( p < 10−4). The regional trends are distributed around the global mean trend, while the fluctuation levels increases when going from global to regional scale. The temperature fluctuations of the tropical oceans are observed to be strongly influenced by El Niño-Southern Oscillation (ENSO) and, therefore, more consistent with autoregressive processes of order 1 [AR(1)]. A likelihood-ratio test is used to systematically determine the best null model [AR(1) or LRD]. About 80% of the regional warming trends are found to be significant (with a 5% significance level). [ABSTRACT FROM AUTHOR]

Of all the major coastal upwelling systems in the world's oceans, the Benguela, located off southwest Africa, is the one that climate models find hardest to simulate well. This paper investigates the sensitivity of upwelling processes, and of sea surface temperature (SST), in this region to resolution of the climate model and to the offshore wind structure. The Community Climate System Model (version 4) is used here, together with the Regional Ocean Modeling System. The main result is that a realistic wind stress curl at the eastern boundary, and a high-resolution ocean model, are required to well simulate the Benguela upwelling system. When the wind stress curl is too broad (as with a 1° atmosphere model or coarser), a Sverdrup balance prevails at the eastern boundary, implying southward ocean transport extending as far as 30°S and warm advection. Higher atmosphere resolution, up to 0.5°, does bring the atmospheric jet closer to the coast, but there can be too strong a wind stress curl. The most realistic representation of the upwelling system is found by adjusting the 0.5° atmosphere model wind structure near the coast toward observations, while using an eddy-resolving ocean model. A similar adjustment applied to a 1° ocean model did not show such improvement. Finally, the remote equatorial Atlantic response to restoring SST in a broad region offshore of Benguela is substantial; however, there is not a large response to correcting SST in the narrow coastal upwelling zone alone. [ABSTRACT FROM AUTHOR]

Simulations from a regional climate model (RCM) as part of a superensemble experiment were compared with observations of surface meteorological variables over the western United States. The RCM is the Hadley Centre Regional Climate Model, version 3, with improved physics parameterizations (HadRM3P) run at 25-km resolution and nested within the Hadley Centre Atmosphere Model, version 3 (HadAM3P). Overall, the means of seasonal temperature were well represented in the simulations; 95% of grid points were within 2.7°, 2.4°, and 3.6°C of observations in winter, spring, and summer, respectively. The model was too warm over most of the domain in summer except central California and southern Nevada. HadRM3P produced more extreme temperatures than observed. The overall magnitude and spatial pattern of precipitation were well characterized, though HadRM3P exaggerated the orographic enhancement along the coastal mountains, Cascade Range, and Sierra Nevada. HadRM3P produced warm/dry northwest, cool/wet southwest U.S. patterns associated with El Niño. However, there were notable differences, including the locations of the transition from warm (dry) to cool (wet) in the anomaly fields when compared with observations, though there was disagreement among observations. HadRM3P simulated the observed spatial pattern of mean annual temperature more faithfully than any of the RCM-GCM pairings in the North American Regional Climate Change Assessment Program (NARCCAP). Errors in mean annual precipitation from HadRM3P fell within the range of errors of the NARCCAP models. Last, this paper provided examples of the size of an ensemble required to detect changes at the local level and demonstrated the effect of parameter perturbation on regional precipitation. [ABSTRACT FROM AUTHOR]

This study explores the added value (AV) of a regional Earth system model (ESM) compared to an atmosphere-only regional climate model (RCM) in simulating West African monsoon (WAM) rainfall. The primary goals are to foster discussions on the suitability of coupled RCMs for WAM projections and deepen our understanding of ocean–atmosphere coupling's influence on the WAM system. The study employs results from dynamical downscaling of the ERA-Interim reanalysis and Max Plank Institute ESM, low resolution (MPI-ESM-LR), by two RCMs, atmosphere only (REMO) and REMO coupled with Max Planck Institute Ocean Model (MPIOM) (ROM), at ∼25-km horizontal resolution. Results show that in regions distant from coupling domain boundaries such as West Africa (WA), constraint conditions from ERA-Interim are more beneficial than coupling effects. REMO, reliant on oceanic sea surface temperatures (SSTs) from observations and influenced by ERA-Interim, is biased under coupling conditions, although coupling offers potential advantages in representing heat and mass fluxes. Contrastingly, as intended, coupling improves SSTs and monsoon fluxes' relationships under ESM-forced conditions. In this latter case, the coupling features a dipole-like spatial structure of AV, improving precipitation over the Guinea Coast but degrading precipitation over half of the Sahel. Our extensive examination of physical processes and mechanisms underpinning the WAM system supports the plausibility of AV. Additionally, we found that the monsoonal dynamics over the ocean respond to convective activity, with the Sahara–Sahel surface temperature gradient serving as the maintenance mechanism. While further efforts are needed to enhance the coupled RCM, we advocate for its use in the context of WAM rainfall forecasts and projections. [ABSTRACT FROM AUTHOR]

Of concern to Colorado River management, as operating guidelines post-2026 are being considered, is whether water resource recovery from low flows during 2000–20 is possible. Here, we analyze new simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to determine plausible climate impacts on Colorado River flows for 2026–50 when revised guidelines would operate. We constrain projected flows for Lees Ferry, the gauge through which 85% of the river flow passes, using its estimated sensitivity to meteorological variability together with CMIP6-projected precipitation and temperature changes. The critical importance of precipitation, especially its natural variability, is emphasized. Model projections indicate increased precipitation in the upper Colorado River basin due to climate change, which alone increases river flows by 5%–7% (relative to a 2000–20 climatology). Depending on the river's temperature sensitivity, this wet signal compensates for some, if not all, of the depleting effects of basin warming. Considerable internal decadal precipitation variability (∼5% of the climatological mean) is demonstrated, driving a greater range of plausible Colorado River flow changes for 2026–50 than previously surmised from treatment of temperature impacts alone: the overall precipitation-induced Lees Ferry flow changes span from −25% to +40%, contrasting with a range from −30% to −5% from expected warming effects only. Consequently, extreme low and high flows are more likely. Lees Ferry flow projections, conditioned on initial drought states akin to 2000–20, reveal substantial recovery odds for water resources, albeit with elevated risks of even further flow declines than in recent decades. Significance Statement: Increasing temperatures have led to concerns that Colorado River flows will be permanently reduced due to global warming. Here, we analyze precipitation, surface temperature, and streamflow (at Lees Ferry) in the upper Colorado River basin since 1895 revealing precipitation to be the largest contributor to runoff variability while temperature variability has been a much smaller contributor. New climate model projections indicate increased precipitation in the upper basin due to global warming, acting to increase river flows by 5%–7% during 2026–50. This wet signal compensates for some of the depleting effects from further basinwide warming. Critically, the large intrinsic variability in precipitation is demonstrated to yield a wider range of future Colorado River flows than previously foreseen based solely on considering temperature effects. [ABSTRACT FROM AUTHOR]

Recent research has shown that there is a projected increase in the frequency of strong positive Indian Ocean dipole (spIOD) events in terms of both sea surface temperature (SST) and precipitation. However, it is not clear whether the spIOD events defined by SST and precipitation, hereafter referred to as SST-spIOD and Pr-spIOD events, respectively, will increase at the same pace under greenhouse warming. Using climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and CMIP6 that can reasonably simulate the spIOD events, here we find that the occurrence of Pr-spIOD events increases much more than that of SST-spIOD events (+217% vs +77%) under the future high-emission scenario. The diverse response stems from a notably large increase in the spIOD events with strong rainfall but moderate SST anomalies (Pr-only spIOD), which is facilitated by a change in the local meridional Hadley circulation induced by land–sea thermal contrast in addition to the background mean-state SST warming. For the spIOD events with prominent anomalies in both rainfall and SST (concurrent spIOD), their occurrences are projected to increase, but their average SST amplitude is projected to weaken. The increased occurrence is associated with a westward-extended structural change induced by mean linear zonal advection feedbacks, while the weakened event-average SST amplitude results from a more stabilized atmosphere and coincides with the projected reduction in IOD SST skewness. Our results suggest that IOD-related mitigation strategies should consider the diverse responses of different kinds of spIOD events to greenhouse warming. [ABSTRACT FROM AUTHOR]

Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitime-scale high-efficiency filtering algorithm which includes a deep-ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously established CDA system within the Coupled Model, version 2.1 (CM2-MSHea-CDA), that is developed by the Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past 4 decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep-ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, El Niño–Southern Oscillation (ENSO), and Pacific decadal oscillation (PDO). Particularly, both CDA systems generate similar time mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations. Significance Statement: A coupled climate model simulates the interactions of the atmosphere, ocean, sea ice, and land processes and derives change and variations of the climate system by combining these components. Due to imperfect numerical schemes, a model usually has systematic biases to the real world. Coupled data assimilation (CDA) combines coupled model dynamics and physics with atmospheric and oceanic observations to reconstruct the historical states of Earth's climate system, forming the analysis of climate change and variability. However, because of the existence of model errors and individual data assimilation algorithm behaviors, currently, the ocean variability of reanalysis produced by different numerical systems is basically divergent. Here, we first apply an identical coupled data assimilation algorithm and effective model error relaxing scheme to two widely used IPCC coupled models to establish two CDA systems. Then, we use both CDA systems to assimilate historical atmospheric and oceanic observations to form two sets of climate reanalyses for the past 4 decades. We found, by some degree, that the results of these multimodel coupled climate reanalyses converged to what historical states ought to be in observational estimation. These convergent climate reanalyses are expected to significantly increase our understanding of CDA and climate assessment. [ABSTRACT FROM AUTHOR]

The models that participated in the Coupled Model Intercomparison Project (CMIP) exhibit large biases in Arctic sea ice climatology that seem related to biases in seasonal atmospheric and oceanic circulations. Using historical runs of 34 CMIP6 models from 1979 to 2014, we investigate the links between the climatological sea ice concentration (SIC) biases in September and atmospheric and oceanic model climatologies. The main intermodel spread of September SIC is well described by two leading EOFs, which together explain ∼65% of its variance. The first EOF represents an underestimation or overestimation of SIC in the whole Arctic, while the second EOF describes opposite SIC biases in the Atlantic and Pacific sectors. Regression analysis indicates that the two SIC modes are closely related to departures from the multimodel mean of Arctic surface heat fluxes during summer, primarily shortwave and longwave radiation, with incoming Atlantic Water playing a role in the Atlantic sector. Local and global links with summer cloud cover, low-level humidity, upper or lower troposphere temperature/circulation, and oceanic variables are also found. As illustrated for three climate models, the local relationships with the SIC biases are mostly similar in the Arctic across the models but show varying degrees of Atlantic inflow influence. On a global scale, a strong influence of the summer atmospheric circulation on September SIC is suggested for one of the three models, while the atmospheric influence is primarily via thermodynamics in the other two. Clear links to the North Atlantic oceanic circulation are seen in one of the models. [ABSTRACT FROM AUTHOR]

This study identifies two distinct patterns of the summer Indian Ocean dipole (IOD)—the coastal IOD and the offshore IOD—named based on the proximity of their eastern pole to Sumatra. Their spatial characteristics, evolutionary mechanisms, relationships with ENSO, impacts on precipitation, and the factors controlling the simulation performances of climate models are discussed. The coastal IOD has the same eastern pole as the conventional IOD, which is located off the coast of Sumatra, but its western pole is situated in the central region of the tropical Indian Ocean (TIO). In contrast, the offshore IOD shares the same western pole as the conventional IOD, which is located off the coast of Somalia, whereas its eastern pole is positioned in the central TIO. Regarding their evolutions, while they initially develop similarly, their later evolutions differ due to their distinct pole locations: The offshore IOD peaks in summer, while the coastal IOD can be sustained into autumn. The coastal IOD correlates to preceding and late ENSO states, but the offshore IOD does not, making it an independent internal mode of TIO. The two IODs affect climate differently, with only the coastal IOD affecting Australian rainfall. Climate models exhibit varied levels of performance in simulating the two IODs. Specifically, a stronger link between spring TIO rainfall and ENSO, as well as stronger southeasterly monsoon winds in the southern TIO, can enhance the coastal IOD modeling, while a stronger summer Somali jet benefits the simulation of the offshore IOD. Distinguishing these two IODs has implications for accurate diagnosis and prediction of the summer climate surrounding the TIO. Significance Statement: The Indian Ocean dipole (IOD) significantly influences the monsoon climate. Previous studies use a unified IOD index based on all-season sea surface temperature (SST) data of tropical Indian Ocean (TIO), resulting in a representation biased toward the autumn peak season. However, understanding SST patterns during summer is crucial for Asian summer monsoon precipitation, and what are the characteristic patterns of summer IOD remains unclear. This study fundamentally advances the understanding of the IOD's seasonal diversity by identifying two distinct dipole patterns in the summer TIO. These patterns differ from the conventional IOD in terms of spatial characteristics, evolutions, ENSO relationships, and precipitation impacts. Distinguishing between these two IODs is anticipated to improve the diagnosis and prediction of summer monsoon rainfall. [ABSTRACT FROM AUTHOR]

The variability of stratospheric water vapor (SWV) plays a crucial role in stratospheric chemistry and Earth's energy budget, strongly influenced by sea surface temperature (SST). In this study, we systematically investigate the response of lower-SWV (LSWV) to regional sea surface temperature changes using idealized SST patch experiments within a climate model. The results indicate that LSWV is most sensitive to tropical sea surface temperature, with the strongest response occurring in late autumn and early winter. Warming of the tropical Indian Ocean and western Pacific (WP) leads to stratospheric drying, while warming of the tropical Atlantic (TA) and eastern Pacific results in stratospheric moistening. The drying impact on LSWV due to warming in the western Pacific Ocean exceeds the wet effect in the eastern Pacific Ocean by approximately 60%. The variations in tropical SST influence LSWV by modulating the temperature at the tropical tropopause layer, especially over the Indo-Pacific warm pool through Matsuno–Gill responses. Furthermore, the response of LSWV to tropical SST changes exhibits nonnegligible nonlinearity, which indicates the importance of nonlinearity in determining the LSWV response to global surface warming. Significance Statement: In this study, we explore how changes in the temperature of the ocean's surface can affect the amount of water vapor in the stratosphere, a layer of Earth's atmosphere. Understanding this relationship is important because water vapor in the stratosphere can influence both our climate and the chemistry of the atmosphere. Using a climate model, we found that water vapor in the lower stratosphere is especially responsive to temperature changes in tropical ocean regions. Specifically, when the Indian Ocean and the western Pacific get warmer, the stratosphere tends to get drier. On the other hand, warming in the Atlantic and eastern Pacific leads to more moisture in the stratosphere. The way these changes add up is complex and not simply a sum of individual parts, especially in tropical warm pool regions. Our findings have implications for how we understand and predict the impacts of climate change on stratospheric water vapor. [ABSTRACT FROM AUTHOR]

The amount of rainfall during June and July along the mei-yu front contributes about 45% to the total summer precipitation over the Yangtze River valley. How it will change under global warming is of great concern to the people of China because of its particular socioeconomic importance, but climate model projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) show large uncertainties. This paper examines model resolution sensitivity and reports large differences in projected future summer rainfall along the mei-yu front between a low-resolution (Gaussian N96 grid, ~1.5° latitude–longitude) and a high-resolution (N216, ~0.7°) version of the Hadley Centre’s latest climate model, the HadGEM3 Global Coupled Configuration 2.0 (HadGEM3-GC2). The high-resolution model projects large increases of summer rainfall under two representative concentration pathway scenarios (RCP8.5 and RCP4.5) whereas the low-resolution model shows a decrease. A larger increase of projected mei-yu rainfall in higher-resolution models is also observed across the CMIP5 ensemble. These differences can be explained in terms of enhanced moist static energy advection and moisture convergence by stationary eddies in the high-resolution model. A large-scale manifestation of the anomalous stationary eddies is the contrasting response to the same warming scenario by the western North Pacific subtropical high, which is almost unchanged in N216 but retreats evidently eastward in N96, reducing the southwesterly flow and consequently moisture supply to the mei-yu front. Further increases in model resolution to resolve parameterized processes and detailed orographic features will hopefully reduce the spread in future climate projections. [ABSTRACT FROM AUTHOR]

This paper describes a new method for parameterizing convective cloud fraction that can be used within large-scale climate models, and evaluates the new method using the Regional Climate Model, version 3 (RegCM3), coupled to the land surface scheme Integrated Biosphere Simulator (IBIS). The horizontal extent of convective cloud cover is calculated by utilizing a relationship between the simulated amount of convective cloud water and typical observations of convective cloud water density. This formulation not only provides a physically meaningful basis for the simulation of convective cloud cover, but it is also spatially and temporally variable and independent of model resolution, rendering it generally applicable for large-scale climate models. Simulations over the Maritime Continent show that the new method allows for simulation of an essential convective-radiative feedback, which was absent in the existing version of RegCM3-IBIS, such that moist convection not only responds to diurnal variability at the earth's surface but also impacts the solar radiation received at the surface via cumulus cloud production. The impact on model performance was mixed, but it is considered that appropriate representation of the convective-radiative feedback and improved physical realism resulting from the new cloud fraction parameterization will likely have positive benefits elsewhere. The role of convective rainfall production in the convective-radiative feedback and a new parameterization for convective autoconversion are addressed in Part II of this paper series. [ABSTRACT FROM AUTHOR]

This paper describes climate simulations of the Community Atmosphere Model, version 5 (CAM5), coupled with a higher-order turbulence closure known as Cloud Layers Unified by Binormals (CLUBB). CLUBB is a unified parameterization of the planetary boundary layer (PBL) and shallow convection that is centered around a trivariate probability density function (PDF) and replaces the conventional PBL, shallow convection, and cloud macrophysics schemes in CAM5. CAM-CLUBB improves many aspects of the base state climate compared to CAM5. Chief among them is the transition of stratocumulus to trade wind cumulus regions in the subtropical oceans. In these regions, CAM-CLUBB provides a much more gradual transition that is in better agreement with observational analysis compared to CAM5, which is too abrupt. The improvement seen in CAM-CLUBB can be largely attributed to the gradual evolution of the simulated turbulence, which is in part a result of the unified nature of the parameterization, and to the general improved representation of shallow cumulus clouds compared to CAM5. In addition, there are large differences in the representation and structure of marine boundary layer clouds between CAM-CLUBB and CAM5. CAM-CLUBB is also shown to be more robust, in terms of boundary layer clouds, to changes in vertical resolution for global simulations in a preliminary test. [ABSTRACT FROM AUTHOR]

The Glimmer Community Ice Sheet Model (Glimmer-CISM) has been implemented in the Community Earth System Model (CESM). Glimmer-CISM is forced by a surface mass balance (SMB) computed in multiple elevation classes in the CESM land model and downscaled to the ice sheet grid. Ice sheet evolution is governed by the shallow-ice approximation with thermomechanical coupling and basal sliding. This paper describes and evaluates the initial model implementation for the Greenland Ice Sheet (GIS). The ice sheet model was spun up using the SMB from a coupled CESM simulation with preindustrial forcing. The model's sensitivity to three key ice sheet parameters was explored by running an ensemble of 100 GIS simulations to quasi equilibrium and ranking each simulation based on multiple diagnostics. With reasonable parameter choices, the steady-state GIS geometry is broadly consistent with observations. The simulated ice sheet is too thick and extensive, however, in some marginal regions where the SMB is anomalously positive. The top-ranking simulations were continued using surface forcing from CESM simulations of the twentieth century (1850-2005) and twenty-first century (2005-2100, with RCP8.5 forcing). In these simulations the GIS loses mass, with a resulting global-mean sea level rise of 16 mm during 1850-2005 and 60 mm during 2005-2100. This mass loss is caused mainly by increased ablation near the ice sheet margin, offset by reduced ice discharge to the ocean. Projected sea level rise is sensitive to the initial geometry, showing the importance of realistic geometry in the spun-up ice sheet. [ABSTRACT FROM AUTHOR]

This paper mainly addresses two issues that concern the longwave climate feedbacks. First, it is recognized that the radiative forcing of greenhouse gases, as measured by their impact on the outgoing longwave radiation (OLR), may vary across different climate models even when the concentrations of these gases are identically prescribed. This forcing variation contributes to the discrepancy in these models' projections of surface warming. A method is proposed to account for this effect in diagnosing the sensitivity and feedbacks in the models. Second, it is shown that the stratosphere is an important factor that affects the OLR in transient climate change. Stratospheric water vapor and temperature changes may both act as a positive feedback mechanism during global warming and cannot be fully accounted as a 'stratospheric adjustment' of radiative forcing. Neglecting these two issues may cause a bias in the longwave cloud feedback diagnosed as a residual term in the decomposition of OLR variations. There is no consensus among the climate models on the sign of the longwave cloud feedback after accounting for both issues. [ABSTRACT FROM AUTHOR]

For the first time, a formal comparison is made between gravity wave momentum fluxes in models and those derived from observations. Although gravity waves occur over a wide range of spatial and temporal scales, the focus of this paper is on scales that are being parameterized in present climate models, sub-1000-km scales. Only observational methods that permit derivation of gravity wave momentum fluxes over large geographical areas are discussed, and these are from satellite temperature measurements, constant-density long-duration bal loons, and high-vertical-resolution radiosonde data. The models discussed include two high-resolution models in which gravity waves are explicitly modeled, Kanto and the Community Atmosphere Model, version 5 (CAMS), and three climate models containing gravity wave parameterizations, MAECHAM5, Hadley Centre Global Environmental Model 3 (HadGEM3), and the Goddard Institute for Space Studies (GISS) model. Measurements generally show similar flux magnitudes as in models, except that the fluxes derived from satellite measurements fall off more rapidly with height. This is likely due to limitations on the observable range of wavelengths, although other factors may contribute. When one accounts for this more rapid fall off, the geographical distribution of the fluxes from observations and models compare reasonably well, except for certain features that depend on the specification of the nonorographic gravity wave source functions in the climate models. For instance, both the observed fluxes and those in the high-resolution models are very small at summer high latitudes, but this is not the case for some of the climate models. This comparison between gravity wave fluxes from climate models, high-resolution models, and fluxes derived from observations indicates that such efforts offer a promising path toward improving specifications of gravity wave sources in climate models. [ABSTRACT FROM AUTHOR]

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive procedure for estimating the variability and determining error bars as confidence intervals for climate mean states by accounting for both short- and long-range dependence. While the prevailing methods for quantifying the variability of climate means account for short-range dependence, they ignore long memory, which is demonstrated to lead to underestimated variability and hence artificially narrow confidence intervals. To capture both short- and long-range correlation structures, climate data are modeled as fractionally integrated autoregressive moving-average processes. The preferred model can be selected adaptively via an information criterion and a diagnostic visualization, and the estimated variability of the climate mean state can be computed directly from the chosen model. The procedure was demonstrated by determining error bars for four 30-yr means of surface temperatures observed at Potsdam, Germany, from 1896 to 2015. These error bars are roughly twice the width as those obtained using prevailing methods, which disregard long memory, leading to a substantive reinterpretation of differences among mean states of this particular dataset. Despite their increased width, the new error bars still suggest that a significant increase occurred in the mean temperature state of Potsdam from the 1896–1925 period to the most recent period, 1986–2015. The new wider error bars, therefore, communicate greater uncertainty in the mean state yet present even stronger evidence of a significant temperature increase. These results corroborate a need for more meticulous consideration of the correlation structures of climate data—especially of their long-memory properties—in assessing the variability and determining confidence intervals for their mean states. [ABSTRACT FROM AUTHOR]

In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences. [ABSTRACT FROM AUTHOR]

This paper presents a satellite-observation-based evaluation of the marine boundary layer (MBL) cloud properties from two Community Atmosphere Model, version 5 (CAM5), simulations, one with the standard parameterization schemes (CAM5–Base) and the other with the Cloud Layers Unified by Binormals scheme (CAM5–CLUBB). When comparing the direct model outputs, the authors find that CAM5–CLUBB produces more MBL clouds, a smoother transition from stratocumulus to cumulus, and a tighter correlation between in-cloud water and cloud fraction than CAM5–Base. In the model-to-observation comparison using the COSP satellite simulators, the authors find that both simulations capture the main features and spatial patterns of the observed cloud fraction from MODIS and shortwave cloud radiative forcing (SWCF) from CERES. However, CAM5–CLUBB suffers more than CAM5–Base from a problem that can be best summarized as “undetectable” clouds (i.e., a significant fraction of simulated MBL clouds are thinner than the MODIS detection threshold). This issue leads to a smaller COSP–MODIS cloud fraction and a weaker SWCF in CAM5–CLUBB than the observations and also CAM5–Base in the tropical descending regions. Finally, the authors compare modeled radar reflectivity with CloudSat observations and find that both simulations, especially CAM5–CLUBB, suffer from an excessive drizzle problem. Further analysis reveals that the subgrid precipitation enhancement factors in CAM5–CLUBB are unrealistically large, which makes MBL clouds precipitate too excessively, and in turn results in too many undetectable thin clouds. [ABSTRACT FROM AUTHOR]

This paper introduces and applies a new method to consistently estimate internal climate variability for all models within a multimodel ensemble. The method regresses each model's estimate of internal variability from the preindustrial control simulation on the variability derived from a model's ensemble simulations, thus providing practical evidence of the quasi-ergodic assumption. The method allows one to test in a multimodel consensus view how the internal variability of a variable changes for different forcing scenarios. Applying the method to the CMIP5 model ensemble shows that the internal variability of global-mean surface air temperature remains largely unchanged for historical simulations and might decrease for future simulations with a large CO2 forcing. Regionally, the projected changes reveal likely increases in temperature variability in the tropics, subtropics, and polar regions, and extremely likely decreases in midlatitudes. Applying the method to sea ice volume and area shows that their respective internal variability likely or extremely likely decreases proportionally to their mean state, except for Arctic sea ice area, which shows no consistent change across models. For the evaluation of CMIP5 simulations of Arctic and Antarctic sea ice, the method confirms that internal variability can explain most of the models' deviation from observed trends but often not the models' deviation from the observed mean states. The new method benefits from a large number of models and long preindustrial control simulations, but it requires only a small number of ensemble simulations. The method allows for consistent consideration of internal variability in multimodel studies and thus fosters understanding of the role of internal variability in a changing climate. [ABSTRACT FROM AUTHOR]

In a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear to be mainly due to a misunderstanding of the aims of Cheung et al. [ABSTRACT FROM AUTHOR]