Evaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate Model Assessment Tool (CMATv1)

An objective approach is presented for scoring coupled climate simulations through an evaluation against satellite and reanalysis datasets during the satellite era (i.e., since 1979). The approach is motivated, described, and applied to available Coupled Model Intercomparison Project (CMIP) archives and the Community Earth System Model (CESM) Version&nbsp;1 Large Ensemble archives with the goal of robustly benchmarking model performance and its evolution across CMIP generations. A scoring system is employed that minimizes sensitivity to internal variability, external forcings, and model tuning. Scores are based on pattern correlations of the simulated mean state, seasonal contrasts, and ENSO teleconnections. A broad range of feedback-relevant fields is considered and summarized on discrete timescales (climatology, seasonal, interannual) and physical realms (energy budget, water cycle, dynamics). Fields are also generally chosen for which observational uncertainty is small compared to model structural differences. Highest mean variable scores across models are reported for well-observed fields such as sea level pressure, precipitable water, and outgoing longwave radiation, while the lowest scores are reported for 500&thinsp;hPa vertical velocity, net surface energy flux, and precipitation minus evaporation. The fidelity of models is found to vary widely both within and across CMIP generations. Systematic increases in model fidelity in more recent CMIP generations are identified, with the greatest improvements occurring in dynamic and energetic fields. Such examples include shortwave cloud forcing and 500&thinsp;hPa eddy geopotential height and relative humidity. Improvements in ENSO scores with time are substantially greater than for climatology or seasonal timescales. Analysis output data generated by this approach are made freely available online from a broad range of model ensembles, including the CMIP archives and various single-model large ensembles. These multimodel archives allow for an expeditious analysis of performance across a range of simulations, while the CESM large ensemble archive allows for estimation of the influence of internal variability on computed scores. The entire output archive, updated and expanded regularly, can be accessed at http://webext.cgd.ucar.edu/Multi-Case/CMAT/index.html (last access: 18 August 2020). </div

Attribution of climate extreme events

There is a tremendous desire to attribute causes to weather and climate events that is often challenging from a physical standpoint. Headlines attributing an event solely to either human-induced climate change or natural variability can be misleading when both are invariably in play. The conventional attribution framework struggles with dynamically driven extremes because of the small signal-to-noise ratios and often uncertain nature of the forced changes. Here, we suggest that a different framing is desirable, which asks why such extremes unfold the way they do. Specifically, we suggest that it is more useful to regard the extreme circulation regime or weather event as being largely unaffected by climate change, and question whether known changes in the climate system's thermodynamic state affected the impact of the particular event. Some examples briefly illustrated include 'snowmaggedon' in February 2010, superstorm Sandy in October 2012 and supertyphoon Haiyan in November 2013, and, in more detail, the Boulder floods of September 2013, all of which were influenced by high sea surface temperatures that had a discernible human component

Sea level Projections with Machine Learning using Altimetry and Climate Model ensembles

Satellite altimeter observations retrieved since 1993 show that the global mean sea level is rising at an unprecedented rate (3.4mm/year). With almost three decades of observations, we can now investigate the contributions of anthropogenic climate-change signals such as greenhouse gases, aerosols, and biomass burning in this rising sea level. We use machine learning (ML) to investigate future patterns of sea level change. To understand the extent of contributions from the climate-change signals, and to help in forecasting sea level change in the future, we turn to climate model simulations. This work presents a machine learning framework that exploits both satellite observations and climate model simulations to generate sea level rise projections at a 2-degree resolution spatial grid, 30 years into the future. We train fully connected neural networks (FCNNs) to predict altimeter values through a non-linear fusion of the climate model hindcasts (for 1993-2019). The learned FCNNs are then applied to future climate model projections to predict future sea level patterns. We propose segmenting our spatial dataset into meaningful clusters and show that clustering helps to improve predictions of our ML model

On the Relationship between Regional Ocean Heat Content and Sea Surface Height

An accurate diagnosis of ocean heat content (OHC) is essential for interpreting climate variability and change, as evidenced for example by the broad range of hypotheses that exists for explaining the recent hiatus in global mean surface warming. Potential insights are explored here by examining relationships between OHC and sea surface height (SSH) in observations and two recently available large ensembles of climate model simulations from the mid-twentieth century to 2100. It is found that in decadal-length observations and a model control simulation with constant forcing, strong ties between OHC and SSH exist, with little temporal or spatial complexity. Agreement is particularly strong on monthly to interannual time scales. In contrast, in forced transient warming simulations, important dependencies in the relationship exist as a function of region and time scale. Near Antarctica, low-frequency SSH variability is driven mainly by changes in the circumpolar current associated with intensified surface winds, leading to correlations between OHC and SSH that are weak and sometimes negative. In subtropical regions, and near other coastal boundaries, negative correlations are also evident on long time scales and are associated with the accumulated effects of changes in the water cycle and ocean dynamics that underlie complexity in the OHC relationship to SSH. Low-frequency variability in observations is found to exhibit similar negative correlations. Combined with altimeter data, these results provide evidence that SSH increases in the Indian and western Pacific Oceans during the hiatus are suggestive of substantial OHC increases. Methods for developing the applicability of altimetry as a constraint on OHC more generally are also discussed

Paleoclimate constraints on the spatiotemporal character of past and future droughts

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 33(22), (2020): 9883-9903, https://doi.org/10.1175/JCLI-D-20-0004.1.Machine-learning-based methods that identify drought in three-dimensional space–time are applied to climate model simulations and tree-ring-based reconstructions of hydroclimate over the Northern Hemisphere extratropics for the past 1000 years, as well as twenty-first-century projections. Analyzing reconstructed and simulated drought in this context provides a paleoclimate constraint on the spatiotemporal characteristics of simulated droughts. Climate models project that there will be large increases in the persistence and severity of droughts over the coming century, but with little change in their spatial extent. Nevertheless, climate models exhibit biases in the spatiotemporal characteristics of persistent and severe droughts over parts of the Northern Hemisphere. We use the paleoclimate record and results from a linear inverse modeling-based framework to conclude that climate models underestimate the range of potential future hydroclimate states. Complicating this picture, however, are divergent changes in the characteristics of persistent and severe droughts when quantified using different hydroclimate metrics. Collectively our results imply that these divergent responses and the aforementioned biases must be better understood if we are to increase confidence in future hydroclimate projections. Importantly, the novel framework presented herein can be applied to other climate features to robustly describe their spatiotemporal characteristics and provide constraints on future changes to those characteristics.This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977. JAF was also supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy's Office of Biological & Environmental Research (BER) via National Science Foundation IA 1844590. JS was supported in part by the U.S. National Science Foundation through Grants AGS-1602920 and AGS-1805490, and by the National Oceanic and Atmospheric Administration by Grant NA20OAR4310425. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portal. We thank the editor and two reviewers for comments that greatly improved the quality of this manuscript. This is SOEST Publication No. 11116 and LDEO Publication No. 8450.2021-04-1

Potential Influences of Volcanic Eruptions on Future Global Land Monsoon Precipitation Changes

The global monsoon system is of exceptional socioeconomic importance owing to its impacts on two-thirds of the globe’s population. Major volcanic eruptions strongly influence global land monsoon (GLM) precipitation change. By using 60 plausible eruption scenarios sampled from reconstructed volcanic proxies over the past 2,500 years, 21st century volcanic influences on GLM precipitation projections are examined with an Earth system model under a moderate emission scenario. The decadal-scale ensemble spread with realistic eruptions (VOLC) increases by 17.5% and 20.1% compared to no-volcanic (NO-VOLC) and constant background-volcanic (VOLC-CONST) scenarios, respectively. Compared with NO-VOLC, the centennial mean VOLC GLM precipitation shows a 10% overall reduction and regionally, Asia is the most impacted. Changes in atmospheric circulation in the aftermath of large volcanic eruptions match the global warming response patterns well with opposite sign, with the North American monsoon precipitation enhanced following large volcanic eruptions, which is in sharp contrast to the robust decrease in Asian monsoon rainfall. Volcanic activity could delay the time of emergence of anthropogenic influence by five years on average over about 60% of the GLM area. Our results demonstrate the importance of statistical representation of potential volcanism for the projections of future monsoon variability. Quantifying volcanic impacts on regional climate projections and their socioeconomic influences on infrastructure planning, food security, and disaster management should be a priority of future work.publishedVersio

The Maunder minimum and the Little Ice Age: an update from recent reconstructions and climate simulations

The Maunder minimum (MM) was a period of extremely low solar activity from approximately AD 1650 to 1715. In the solar physics literature, the MM is sometimes associated with a period of cooler global temperatures, referred to as the Little Ice Age (LIA), and thus taken as compelling evidence of a large, direct solar influence on climate. In this study, we bring together existing simulation and observational studies, particularly the most recent solar activity and paleoclimate reconstructions, to examine this relation. Using northern hemisphere surface air temperature reconstructions, the LIA can be most readily defined as an approximately 480 year period spanning AD 1440–1920, although not all of this period was notably cold. While the MM occurred within the much longer LIA period, the timing of the features are not suggestive of causation and should not, in isolation, be used as evidence of significant solar forcing of climate. Climate model simulations suggest multiple factors, particularly volcanic activity, were crucial for causing the cooler temperatures in the northern hemisphere during the LIA. A reduction in total solar irradiance likely contributed to the LIA at a level comparable to changing land use

ENSO-driven energy budget perturbations in observations and CMIP models

Various observation-based datasets are employed to robustly quantify changes in ocean heat content (OHC), anomalous ocean–atmosphere energy exchanges and atmospheric energy transports during El Niño-Southern Oscillation (ENSO). These results are used as a benchmark to evaluate the energy pathways during ENSO as simulated by coupled climate model runs from the CMIP3 and CMIP5 archives. The models are able to qualitatively reproduce observed patterns of ENSO-related energy budget variability to some degree, but key aspects are seriously biased. Area-averaged tropical Pacific OHC variability associated with ENSO is greatly underestimated by all models because of strongly biased responses of net radiation at top-of-the-atmosphere to ENSO. The latter are related to biases of mean convective activity in the models and project on surface energy fluxes in the eastern Pacific Intertropical Convergence Zone region. Moreover, models underestimate horizontal and vertical OHC redistribution in association with the generally too weak Bjerknes feedback, leading to a modeled ENSO affecting a too shallow layer of the Pacific. Vertical links between SST and OHC variability are too weak even in models driven with observed winds, indicating shortcomings of the ocean models. Furthermore, modeled teleconnections as measured by tropical Atlantic OHC variability are too weak and the tropical zonal mean ENSO signal is strongly underestimated or even completely missing in most of the considered models. Results suggest that attempts to infer insight about climate sensitivity from ENSO-related variability are likely to be hampered by biases in ENSO in CMIP simulations that do not bear a clear link to future changes

Origin of interannual variability in global mean sea level

Author Posting. © National Academy of Sciences, 2020. This article is posted here by permission of National Academy of Sciences for personal use, not for redistribution. The definitive version was published in Proceedings of the National Academy of Sciences of the United States of America 117(25), (2020): 13983-13990, doi: 10.1073/pnas.1922190117.The two dominant drivers of the global mean sea level (GMSL) variability at interannual timescales are steric changes due to changes in ocean heat content and barystatic changes due to the exchange of water mass between land and ocean. With Gravity Recovery and Climate Experiment (GRACE) satellites and Argo profiling floats, it has been possible to measure the relative steric and barystatic contributions to GMSL since 2004. While efforts to “close the GMSL budget” with satellite altimetry and other observing systems have been largely successful with regards to trends, the short time period covered by these records prohibits a full understanding of the drivers of interannual to decadal variability in GMSL. One particular area of focus is the link between variations in the El Niño−Southern Oscillation (ENSO) and GMSL. Recent literature disagrees on the relative importance of steric and barystatic contributions to interannual to decadal variability in GMSL. Here, we use a multivariate data analysis technique to estimate variability in barystatic and steric contributions to GMSL back to 1982. These independent estimates explain most of the observed interannual variability in satellite altimeter-measured GMSL. Both processes, which are highly correlated with ENSO variations, contribute about equally to observed interannual GMSL variability. A theoretical scaling analysis corroborates the observational results. The improved understanding of the origins of interannual variability in GMSL has important implications for our understanding of long-term trends in sea level, the hydrological cycle, and the planet’s radiation imbalance.The research was carried out at JPL, California Institute of Technology, under a contract with NASA. This study was funded by NASA Grants NNX17AH35G (Ocean Surface Topography Science Team), 80NSSC17K0564, and 80NSSC17K0565 (NASA Sea Level Change Team). The efforts of J.T.F. in this work were also supported by NSF Award AGS-1419571, and by the Regional and Global Model Analysis component of the Earth and Environmental System Modeling Program of the US Department of Energy's Office of Biological & Environmental Research via National Science Foundation Grant IA 1844590. C.G.P. was supported by the J. Lamar Worzel Assistant Scientist Fund and the Penzance Endowed Fund in Support of Assistant Scientists at the Woods Hole Oceanographic Institution.2020-12-0

Asymmetric Response of Land Storage to ENSO Phase and Duration

Emergence of global mean sea level (GMSL) from a 'hiatus' following a persistent La Ni&ntilde;a highlights the need to understand the causes of interannual variability in GMSL. Several studies link interannual variability in GMSL to anomalous transport of water mass between land and ocean-and subsequent changes in water storage in these reservoirs-primarily driven by El Ni&ntilde;o/Southern Oscillation (ENSO). Despite this, asymmetries in teleconnections between ENSO mode and land water storage have received less attention. We use historical simulations of natural climate variability to characterize asymmetries in the hydrological response to ENSO based on phase and duration. Findings indicate pronounced phase-specific and duration-specific asymmetries covering up to 93 and 50 million km2 land area, respectively. The asymmetries are seasonally dependent, and based on the mean regional climate are capable of influencing inherently bounded storage by pushing the storage-precipitation relationship towards nonlinearity. The nonlinearities are more pronounced in dry regions in the dry season, wet regions in the wet season, and during Year 2 of persistent ENSO events, limiting the magnitude of associated anomalies under persistent ENSO influence. The findings have implications for a range of stakeholders, including sea level researchers and water managers.</p