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The Early Anthropogenic Hypothesis posits that carbon emissions from ancient farming caused global warming by raising greenhouse gas concentrations (GHG) during the late-Holocene, in contrast to declining GHG during prior interglacials. Here, we explore whether this hypothesized pre-industrial anthropogenic climate change also fostered agriculture by creating more favorable growing conditions. We investigate this question using transient GCM experiments and the Cultivation Suitability Index, CSI, which quantifies farming potential based on climatic and soil factors. The Community Earth System Model (CESM) simulated the climate of the last 6000 years under two alternative forcing scenarios: (1) ACTUAL: orbital variations, historical land cover change, and observed GHG increase; and (2) NATURAL: orbital variations, fixed (natural) land cover, and expected natural GHG decline. The CSI was computed using CESM model output and observed soil properties. Ancient land clearance affected the simulated climate both biogeochemically (via carbon emissions) and biogeophysically (altered surface albedo and land-atmosphere energy fluxes). Biogeochemical effects generally dominated, as evidenced by a warmer (and slightly wetter) global climate in ACTUAL versus NATURAL by year 1850. But a few regions were cooler in ACTUAL, especially interior Eurasia during winter-spring, due to a higher surface albedo from cropland. The expansion of agriculture generally mitigated the orbitally induced decline in cultivation potential in boreal extratropics but worsened it in low latitudes. Our results suggest that ancient farming may have thus promoted a “push-pull” migration during the late-Holocene by inducing climate changes that encouraged a northward spread of agriculture.

Archaeological records document a significant expansion of populations from the Last Glacial Maximum (LGM, ~23-19 ka) to the early Holocene (EH, ~9 ka) in Eurasia, which is often attributed to the influence of orbital-scale climate changes. Yet, information remains limited concerning the climatic factor(s) which were responsible for conditioning demographic patterns. Here, we present results from an improved Minimalist Terrestrial Resource Model (MTRM), forced by a transient climate simulation from the LGM to the EH. Simulated potential hunter-gatherer population densities and spatial distributions across Eurasia are supported by observed archaeological sites in Europe and China. In the low latitudes, potential population size change was predominantly controlled by precipitation and its strong influence on plant and animal resources. In the middle-high latitudes, temperature was the dominant driver in influencing potential population size change and animal resource availability. Different regional responses of potential populations to climate change across Eurasia - owing to variations in available food resources between the LGM and EH - provide a better understanding of human dispersal during the Late Pleistocene., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Recent climate change in the Arctic has been rapid and dramatic, leading to numerous physical and societal consequences. Many studies have investigated these ongoing and projected future changes across a range of climatic variables, but surprisingly little attention has been paid to wind speed, despite its known importance for sea ice motion, ocean wave heights, and coastal erosion. Here we analyzed future trends in Arctic surface wind speed and its relationship with sea ice cover among CMIP5 global climate models. There is a strong anticorrelation between climatological sea ice concentration and wind speed in the early 21 st -century reference climate, and the vast majority of models simulate widespread future strengthening of surface winds over the Arctic Ocean (annual multi-model mean trend of up to 0.8 m s -1 or 13%). Nearly all models produce an inverse relationship between projected changes in sea ice cover and wind speed, such that grid cells with virtually total ice loss almost always experience stronger winds. Consistent with the largest regional ice losses during autumn and winter, the greatest increases in future wind speeds are expected during these two seasons, with localized strengthening up to 23%. As in other studies, stronger surface winds cannot be attributed to tighter pressure gradients but rather to some combination of weakened atmospheric stability and reduced surface roughness as the surface warms and melts. The intermodel spread of wind speed changes, as expressed by the two most contrasting model results, appears to stem from differences in the treatment of surface roughness.

The Arctic is undergoing a pronounced and rapid transformation in response to changing greenhouse gases, including reduction in both sea ice spatial extent and sea ice thickness and projected increase in near-surface winds. Other studies have suggested that the increasing winds may be driven by changing surface roughness and/or stability, but the coupled relationship between the changing winds and changing sea ice in different regions and seasons is not yet well understood. This study disentangles ways in which changing surface conditions affect the changing winds regionally and seasonally. We analyze 50 experiments from the Community Earth System Model version 2 Large Ensemble (CESM2-LE) as well as 5 experiments using CESM2 in which sea ice roughness has been decreased to be equivalent to what it would be over the smoother, open ocean (SMOOTH). We find that with a smoother surface there is a small reduction in the surface momentum flux, leading to faster mean wind speeds and slower mean ice speeds. In SMOOTH, the reduction in surface roughness due to ice loss is negligible, resulting in weaker increasing trends in near-surface winds, particularly in Autumn and Winter. There are no significant coupled impacts of surface roughness change on the sea ice mean state or loss trend. Because both the CESM2-LE and SMOOTH experience statistically indistinguishable sea ice loss trends that impact near-surface atmospheric temperatures, the experiments have very similar changes in atmospheric stability which also influences the near-surface winds. Therefore, in CESM2-LE, the near-surface increasing wind speed trends result from both decreases in surface roughness and reduction in near-surface atmospheric stability that are the result of sea ice loss.

Extreme weather events in Asia have been occurring with increasing frequency as the globe warms in response to rising concentrations of greenhouse gases. Many of these events arise from weather regimes that persist over a region for days or even weeks, resulting in disruptive heatwaves, droughts, flooding, snowfalls, and cold spells. We investigate changes in the persistence of large-scale weather systems through a pattern-recognition approach based on daily 500 hPa geopotential height anomalies over the Asian continent. By tracking consecutive days that the atmosphere resides in a particular pattern, we identify long-duration events (LDEs), defined as lasting longer than three days, and measure their frequency of occurrence over time in each pattern. We find that regimes featuring positive height anomalies in high latitudes are occurring more often as the Arctic warms faster than mid-latitudes, both in the recent past and in model projections for the twenty-first century assuming unabated greenhouse gas emissions. The increased dominance of these patterns corresponds to a higher likelihood of LDEs, suggesting that persistent weather conditions will occur more frequently. By mapping observed temperature and precipitation extremes onto each atmospheric regime, we gain insight into the types of disruptive weather events that will become more prevalent as particular patterns become more common.

Arctic neoglaciation following the Holocene Thermal Maximum is an important feature of late-Holocene climate. We investigated this phenomenon using a transient 6000-year simulation with the CESM-CAM5 climate model driven by orbital forcing, greenhouse gas concentrations, and a land use reconstruction. During the first three millennia analyzed here (6–3 ka), mean Arctic snow depth increases, despite enhanced greenhouse forcing. Superimposed on this secular trend is a very abrupt increase in snow depth between 5 and 4.9 ka on Ellesmere Island and the Greenland coasts, in rough agreement with the timing of observed neoglaciation in the region. This transition is especially extreme on Ellesmere Island, where end-of-summer snow coverage jumps from nearly 0 to virtually 100% in 1 year, and snow depth increases to the model’s imposed maximum within 15 years. This climatic shift involves more than the Milankovitch-based expectation of cooler summers causing less snow melt. Coincident with the onset of the cold regime are two consecutive summers with heavy snowfall on Ellesmere Island that help to short-circuit the normal seasonal melt cycle. These heavy snow seasons are caused by synoptic-scale, cyclonic circulation anomalies over the Arctic Ocean and Canadian Archipelago, including an extremely positive phase of the Arctic Oscillation. Our study reveals that a climate model can produce sudden climatic transitions in this region prone to glacial inception and exceptional variability, due to a dynamic mechanism (more summer snowfall induced by an extreme circulation anomaly) that augments the traditional Milankovitch thermodynamic explanation of orbitally induced glacier development.

The term "weather whiplash" was recently coined to describe abrupt swings in weather conditions from one extreme to another, such as from a frigid cold spell to anomalous warmth or from drought to ...

As Earth’s largest collection of fresh water, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Horizontal thermal advection plays an especially prominent role in affecting winter climate over continental interiors, where both climatological conditions and extreme weather are strongly regulated by transport of remote air masses. Interior North America is one such region, and it experiences occasional cold-air outbreaks (CAOs) that may be related to amplified Arctic warming. Despite the known importance of dynamics in shaping the winter climate of this sector and the potential for climate change to modify heat transport, limited attention has been paid to the regional impact of thermal advection. Here, we use a reanalysis product and output from the Community Earth System Model’s Large Ensemble to quantify the roles of zonal and meridional temperature advection over the central United States during winter, both in the late twentieth and late twenty-first centuries. We frame our findings as a “tug-of-war” between opposing influences of the two advection components and between these dynamical forcings vs. thermodynamic changes under greenhouse warming. During both historical and future periods, zonal temperature advection is stronger than meridional advection east of the Rockies. The model simulates a future weakening of both zonal and meridional temperature advection, such that westerly flow provides less warming and northerly flow less cooling. On the most extreme cold days, meridional cold-air advection is more important than zonal warm-air advection. CAOs in the future feature stronger northerly flow but less extreme temperatures (even relative to the warmer climate), indicating the importance of other mechanisms such as snow cover and sea ice changes.

The diminishing Arctic sea ice pack has been widely studied, but previous research has mostly focused on time-mean changes in sea ice rather than on short-term variations that also have important physical and societal consequences. In this study we test the hypothesis that future interannual Arctic sea ice area variability will increase by utilizing 40 independent simulations from the Community Earth System Model's Large Ensemble (CESM-LE) for the 1920–2100 period and augment this with simulations from 12 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Both CESM-LE and CMIP5 models project that ice area variability will indeed grow substantially but not monotonically in every month. There is also a strong seasonal dependence in the magnitude and timing of future variability increases that is robust among CESM ensemble members. The variability generally correlates with the average ice retreat rate, before there is an eventual disappearance in both terms as the ice pack becomes seasonal in summer and autumn by late century. The peak in variability correlates best with the total area of ice between 0.2 and 0.6 m monthly thickness, indicating that substantial future thinning of the ice pack is required before variability maximizes. Within this range, the most favorable thickness for high areal variability depends on the season, especially whether ice growth or ice retreat processes dominate. Our findings suggest that thermodynamic melting (top, bottom, lateral) and growth (frazil, congelation) processes are more important than dynamical mechanisms, namely ice export and ridging, in controlling ice area variability.

Previous research has identified significant and highly variable summertime (July–August–September) warming trends across the Great Lakes, with critical implications for aquatic ecosystems. However, these analyses of long-term warming trends have generally been constrained by the short duration or coarse spatial resolution of available observational datasets. Here, we integrate two existing datasets of Great Lakes surface temperature (LSWT) to evaluate long-term warming trends during 1982–2012 at fine spatial scales and understand the roles of lake bathymetry and climatic factors in regulating the spatially heterogeneous warming rates with the aid of regional climate modeling. Our results show amplified warming in Lake Superior, central-northern Lake Michigan, and central Lake Huron, and muted lake warming elsewhere. This spatial heterogeneity in summertime lake warming is primarily ascribed to the interplay of lake bathymetry and climatological springtime (April–May–June) air temperature. The climatological air temperature strongly influences the relationship between lake warming rates and bathymetry, as the summertime warming rates increase markedly with greater lake depth in the relatively cold environment of Lake Superior but change little in the warmer environment of Lake Ontario. This conditional dependence on background temperature has important implications for understanding and predicting global lake temperature trends.

Projections of Arctic sea ice through the end of the twenty-first century indicate the likelihood of a strong reduction in ice area and thickness in all seasons, leading to a substantial thermodynamic influence on the overlying atmosphere. This is likely to have an effect on winds over the Arctic basin because of changes in atmospheric stability, surface roughness, and/or baroclinicity. Here we identify patterns of wind changes in all seasons across the Arctic and their likely causal mechanisms, particularly those associated with sea ice loss. Output from the Community Earth System Model Large Ensemble Project (CESM-LE) was analyzed for the recent past (primarily 1971–2000) and future (2071–2100). Mean near-surface wind speeds over the Arctic Ocean are projected to increase by late century in all seasons but especially during autumn and winter, when they strengthen by up to 50% locally. The most extreme wind speeds in the 95th percentile change even more, increasing in frequency by up to 100%. The strengthened winds are closely linked to decreasing surface roughness and lower-tropospheric stability resulting from the loss of sea ice cover and consequent surface warming (exceeding 20°C warmer in the central Arctic in autumn and winter), as well as local changes in the storm track. The implications of stronger future winds include increased coastal and navigational hazards. Our findings suggest that increasing winds, along with reduction of sea ice, rising sea level, and thawing permafrost, represent another important contributor to the growing problem of Arctic coastal erosion.

The Marine Isotope Stage 19c (MIS19c) interglaciation is regarded as the best orbital analog to the Holocene. The close of MIS19c (~777,000 years ago) thus serves as a proxy for a contemporary climate system unaffected by humans. Our global climate model simulation driven by orbital parameters and observed greenhouse gas concentrations at the end of MIS19c is 1.3 K colder than the reference pre-industrial climate of the late Holocene (year 1850). Much stronger cooling occurs in the Arctic, where sea ice and year-round snow cover expand considerably. Inferred regions of glaciation develop across northeastern Siberia, northwestern North America, and the Canadian Archipelago. These locations are consistent with evidence from past glacial inceptions and are favored by atmospheric circulation changes that reduce ablation of snow cover and increase accumulation of snowfall. Particularly large buildups of snow depth coincide with presumed glacial nucleation sites, including Baffin Island and the northeast Canadian Archipelago. These findings suggest that present-day climate would be susceptible to glacial inception if greenhouse gas concentrations were as low as they were at the end of MIS 19c.

The rapidly warming Arctic climate may affect weather in middle latitudes, but controversies remain as to mechanisms and robustness. Here, I synthesize recent advances in this rapidly changing field and summarize recommendations on paths forward. Initial “black-and-white” debates about whether Arctic amplification (AA) affects mid-latitude circulation have evolved toward a more nuanced perspective awash in gray. Recent research has demonstrated myriad ways in which AA can influence weather remotely and explored whether any Arctic-based signal is significant against the backdrop of natural variability. The popularity of and controversies surrounding this topic have spurred a multitude of approaches and often-conflicting studies that have widened the envelope of our understanding but hindered a scientific consensus. This messy but necessary exploratory phase of independent investigations is benefiting from recent efforts by the research community to self-organize through workshops, working groups, and coordinated experiments.

In this article, we first re‐examined the characteristics of southern Peru precipitation and its relationship with ENSO by using newly collected rain gauge data and reanalysis data sets. Then we reviewed the current forecast skill of existing and potential existed forecasts. At last we introduced two simple statistical models to predict southern Peru precipitation anomalies at both regional and station levels, with both models demonstrating improvement over existing models based on retrospective forecast experiments.

Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January–March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño–Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit–miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet–dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous USA and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated area-under-the-curve AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species' potential distributions may have already shifted due recent climate change. However, long-term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short-term climate variability are not available, long-term climate information is a sufficient predictor of species distributions in many cases. However, short-term climate variability data may provide information not captured with long-term climate data for use in SDMs.

Climate change is a major challenge for managers of protected areas world-wide, and managers need information about future climate conditions within protected areas. Prior studies of climate change effects in protected areas have largely focused on average climatic conditions. However, extreme weather may have stronger effects on wildlife populations and habitats than changes in averages. Our goal was to quantify future changes in the frequency of extreme heat, drought, and false springs, during the avian breeding season, in 415 National Wildlife Refuges in the conterminous United States. We analyzed spatially detailed data on extreme weather frequencies during the historical period (1950–2005) and under different scenarios of future climate change by mid- and late-21st century. We found that all wildlife refuges will likely experience substantial changes in the frequencies of extreme weather, but the types of projected changes differed among refuges. Extreme heat is projected to increase dramatically in all wildlife refuges, whereas changes in droughts and false springs are projected to increase or decrease on a regional basis. Half of all wildlife refuges are projected to see increases in frequency (> 20% higher than the current rate) in at least two types of weather extremes by mid-century. Wildlife refuges in the Southwest and Pacific Southwest are projected to exhibit the fastest rates of change, and may deserve extra attention. Climate change adaptation strategies in protected areas, such as the U.S. wildlife refuges, may need to seriously consider future changes in extreme weather, including the considerable spatial variation of these changes.

Global warming is expected to affect mid-latitude atmospheric dynamics through changes in the equator-to-pole temperature gradient. While the latitudinal expansion of the tropics would induce both a poleward shift and reinforcement of the westerlies, Arctic changes might counterbalance this effect. Beyond position and speed, potential changes in the flow waviness are crucial for midlatitude weather. Here we investigate such changes through an intuitive metric characterizing the flow sinuosity at 50 °N. We find that despite a slight increase in recent reanalyses, the mid-latitude sinuosity is projected to decrease in response to climate change according to CMIP5 simulations. Recent trends could therefore result from internal variability or different timings of tropical and polar influences. Future uncertainties are dominated by model discrepancies and partially linked to the dispersion in the equator-to-pole temperature gradient response. Our results support the hypothesis that a faster westerly flow is expected to be less sinuous (and vice-versa).

Avian populations can respond dramatically to extreme weather such as droughts and heat waves, yet patterns of response to weather at broad scales remain largely unknown. Our goal was to evaluate annual variation in abundance of 14 grassland bird species breeding in the northern mixed-grass prairie in relation to annual variation in precipitation and temperature. We modeled avian abundance during the breeding season using North American Breeding Bird Survey (BBS) data for the U.S. Badlands and Prairies Bird Conservation Region (BCR 17) from 1980 to 2012. We used hierarchical Bayesian methods to fit models and estimate the candidate weather parameters standardized precipitation index (SPI) and standardized temperature index (STI) for the same year and the previous year. Upland Sandpiper (Bartramia longicauda) responded positively to within-year STI (β = 0.101), and Baird's Sparrow (Ammodramus bairdii) responded negatively to within-year STI (β = −0.161) and positively to within-year SPI (β = 0.195...

The primary drivers of the recent accelerated warming of the Laurentian Great Lakes from 1982 to 2012 are explored through observations, remote sensing, and regional climate model experiments. The study focuses on the abrupt warming from 1997 to 1998 as a proxy for the long-term warming trend. The lake surface warming has been heterogeneous in both space and time, ranging from moderate warming in late spring over the southern lakes and shallow areas of the northern lakes to strong warming in mid-summer over the northern, deep lake areas. The greatest lake warming between 1997 and 1998 occurs over the deepest areas of Lake Superior during mid-summer, primarily arising from enhanced heat accumulation during the mild winter of 1997/1998 and amplified by greater incoming surface solar radiation and air temperature during the spring of 1998, according to model experiments. The mild winter condition, together with the increased solar radiation and air temperature during spring, causes an earlier onset of springtime stratification, resulting in enhanced heat absorption by surface water and thereby contributing to lake surface warming during the subsequent summer in 1998 compared with 1997. In contrast, the modest peak warming over southern lakes and shallow areas of northern lakes from 1997 to 1998 is a rapid response to synchronous increases in solar radiation and air temperature during May between the 2 yr. Changes in antecedent wintertime lake ice cover are found to have played only a minor role in the accelerated warming trend of the Laurentian Great Lakes.

The rapid polar sea ice retreat and its drivers are challenging and still unresolved questions in climate change research. In particular, the relationship between near-surface wind speed and sea ice extent remains unclear for two main reasons: (1) observed wind speeds over Polar Regions are very sparse, and (2) simulated winds by climate models are dependent on subjective parameterizations of boundary layer stratification, ultimately leading to large uncertainty. Here, we use observation-based data (passive microwave sea ice concentration and six different reanalysis datasets) together with output from 26 climate models (from the CMIP5 archive) to quantify the relationships between near-surface wind speed and sea ice concentration over the past 40 years. We find strong inverse relationships between near-surface wind speed and sea ice concentration that are consistent among the six reanalysis datasets. The poleward wind component is particularly increasing in years of reduced sea ice concentration, which contributes to the enhancement of the atmospheric (surface oceanic) poleward heat flux by up to 24 ± 1% (29 ± 2%) in the Arctic and 37 ± 3% (51 ± 3%) in the Antarctic seas, therefore boosting the impact of polar sea ice loss and contributing to polar amplification of climate warming. In addition, our results show a marginal contribution of the dynamical (pushing/opening/compacting) effects of wind on sea ice compared to the thermodynamic effects which in turn play a lower role than the associated change in local surface Autumn–Winter turbulent and Spring–Summer radiative fluxes. Climate models generally produce similar results but with lower magnitude, and one model even simulates the opposite relationship wind/sea-ice. Given the rapid changes in polar climate and the potential impacts on the mid-latitudes, it is urgent that model developments make use of evidence from satellite observations and reanalysis datasets to reduce uncertainties in the representation of relationships between polar winds and sea ice.

Author(s): Peings, Y; Cattiaux, J; Vavrus, SJ; Magnusdottir, G | Abstract: The future response of the atmospheric circulation to increased anthropogenic forcing is uncertain, in particular due to competing influences of the large projected warming at the surface in the Arctic, and at upper-levels in the tropics. In the present study two ensembles of fully-coupled 21st century climate simulations are used to analyze changes in the wintertime eddy-driven jet in the North Atlantic and the relation to the well-defined thermal signatures of climate change. The models project a robust reinforcement of the eddy-driven jet and a decrease in waviness and blockings, that we attribute to a narrowing of the westerly flow in mid-latitudes. Composite analyses suggest that this signal is driven by the opposite influence of Arctic and tropical warming on each flank of the jet. We find that a significant portion of the multi-model spread in the jet metrics can be explained by the ratio between these two signals. The tug-of-war between the two effects influences by how much wintertime cold extremes diminish at the end of the 21st century. Models with dominant tropical warming (i. e. narrower and stronger eddy-driven jet) exhibit less decrease in cold extremes with climate change, due to the maintenance of cooler conditions in the subpolar North Atlantic and subarctic seas compared to models with a predominance of Arctic warming.

The availability of output from climate model ensembles, such as phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), has greatly expanded information about future projections, but there is no accepted blueprint for how this data should be utilized. The multi-model average is the most commonly cited single estimate of future conditions, but higher-order moments representing the variance and skewness of the distribution of projections provide important information about uncertainty. We have analyzed a set of statistically downscaled climate model projections from the CMIP3 archive to assess extreme weather events at a level aimed to be appropriate for decision makers. Our analysis uses the distribution of 13 global climate model projections to derive the inter-model standard deviation, skewness, and percentile ranges for simulated changes in extreme heat, cold, and precipitation by the mid-21st century, based on the A1B emissions scenario. These metrics provide information on overall confidence across the entire range of projections (via the inter-model standard deviation), relative confidence in upper-end versus lower-end changes (via skewness), and quantitative uncertainty bounds (derived from bootstrapping). Over our analysis domain, which covers the northeastern United States and southeastern Canada, some primary findings include: (1) greater confidence in projections of less extreme cold than more extreme heat and intense precipitation, (2) greater confidence in relatively conservative projections of extreme heat, and (3) higher spatial variability in the confidence of projected increases in heavy precipitation. In addition, we describe how a simplified bootstrapping approach can assist decision makers by estimating the probability of changes in extreme weather events based on user-defined percentile thresholds.

Threats to wildlife species from extreme events, such as droughts, are predicted to increase in frequency and magnitude with climate change. Extreme events can cause mortality and community-level changes, but for some mobile species, movement away from areas affected may be a viable option. We examined the effect of extreme weather on spatial patterns of abundance for an irruptive grassland bird species, the Dickcissel (Spiza americana). We calculated route-level annual abundances and abundance anomalies from 1980 to 2012 from North American Breeding Bird Survey data, and classified the Dickcissel’s range into core and edge regions using these abundances. We then compared abundances in the core and edge regions to the standardized precipitation evapotranspiration index, a measure of drought, in linear regressions. We found that Dickcissel irruptions in the northern range edges were related to drought conditions in the range core, potentially a consequence of birds being ‘pushed’ to the range edge when weather was unsuitable. Specifically, Dickcissels moved into refuge sites containing a high proportion of cultivated crops, with higher vegetation greenness, than those areas they leave during drought years. In a changing climate where more frequent extreme weather may be more common, conservation strategies for weather-sensitive species may require consideration of habitat in the edges of species’ ranges, even though non-core areas may be unoccupied in ‘normal’ years. Our results highlight the conservation importance of range edges in providing refuge from extreme events, such as drought, and climate change.

This study tests the hypothesis that Arctic amplification (AA) of global warming remotely affects midlatitudes by promoting a weaker, wavier atmospheric circulation conducive to extreme weather. The investigation is based on the late twenty-first century over greater North America (20°–90°N, 50°–160°W) using 40 simulations from the Community Earth System Model Large Ensemble, spanning 1920–2100. AA is found to promote regionally varying ridging aloft (500 hPa) with strong seasonal differences reflecting the location of the strongest surface thermal forcing. During winter, maximum increases in future geopotential heights are centered over the Arctic Ocean, in conjunction with sea ice loss, but minimum height increases (troughing) occur to the south, over the continental United States. During summer the location of maximum height inflation shifts equatorward, forming an annular band across mid-to-high latitudes of the entire Northern Hemisphere. This band spans the continents, whose enhanced surface heating is aided by antecedent snow-cover loss and reduced terrestrial heat capacity. Through the thermal wind relationship, midtropospheric winds weaken on the equatorward flank of both seasonal ridging anomalies—mainly over Canada during winter and even more over the continental United States during summer—but strengthen elsewhere to form a dipole anomaly pattern in each season. Changes in circulation waviness, expressed as sinuosity, are inversely correlated with changes in zonal wind speed at nearly all latitudes, both in the projections and as observed during recent decades. Over the central United States during summer, the weaker and wavier flow promotes drying and enhanced heating, thus favoring more intense summer weather.

Author(s): Peings, Y; Cattiaux, J; Vavrus, SJ; Magnusdottir, G | Abstract: In the published paper, the quality of the figures has been compromised and so the figures with higher resolution have been added here. (Figure Presented).

Motivated by the documented linkage between water-borne disease outbreaks and heavy rainfall, we compare simulations of precipitation in Wisconsin from two different downscaling procedures (statistical and dynamical) and global climate models (GCMs) for the late 20th and middle 21st centuries (SRES A2 greenhouse emissions scenario). In the inter-model mean, all the three methods produce reasonably accurate simulations of seasonal and annual precipitation amounts during the historical period of 1971–2000 (yearly biases

[1] Increasing attention is being paid to extreme weather, including recent high-profile events involving very destructive cyclones. In summer 2012, a historically powerful cyclone traversed the Arctic, a region experiencing rapid warming and dramatic loss of ice and snow cover. This study addresses whether such powerful storms are an emerging expression of anthropogenic climate change by investigating simulated extreme Arctic cyclones during the historical period (1850–2005) among global climate models in the Coupled Model Intercomparison Project 5 (CMIP5) archive. These general circulation models are able to simulate extreme pressures associated with strong polar storms without a significant dependence on model resolution. The models display realism by generating extreme Arctic storms primarily around subpolar cyclone regions (Aleutian and Icelandic) and preferentially during winter. Simulated secular trends in Arctic mean sea level pressure and extreme cyclones are equivocal; both indicate increasing storminess in some regions, but the magnitude of changes to date are modest compared with future projections.

The Community Atmosphere Model, version 4 (CAM4), was released as part of the Community Climate System Model, version 4 (CCSM4). The finite volume (FV) dynamical core is now the default because of its superior transport and conservation properties. Deep convection parameterization changes include a dilute plume calculation of convective available potential energy (CAPE) and the introduction of convective momentum transport (CMT). An additional cloud fraction calculation is now performed following macrophysical state updates to provide improved thermodynamic consistency. A freeze-drying modification is further made to the cloud fraction calculation in very dry environments (e.g., the Arctic), where cloud fraction and cloud water values were often inconsistent in CAM3. In CAM4 the FV dynamical core further degrades the excessive trade-wind simulation, but reduces zonal stress errors at higher latitudes. Plume dilution alleviates much of the midtropospheric tropical dry biases and reduces the persistent monsoon precipitation biases over the Arabian Peninsula and the southern Indian Ocean. CMT reduces much of the excessive trade-wind biases in eastern ocean basins. CAM4 shows a global reduction in cloud fraction compared to CAM3, primarily as a result of the freeze-drying and improved cloud fraction equilibrium modifications. Regional climate feature improvements include the propagation of stationary waves from the Pacific into midlatitudes and the seasonal frequency of Northern Hemisphere blocking events. A 1° versus 2° horizontal resolution of the FV dynamical core exhibits superior improvements in regional climate features of precipitation and surface stress. Improvements in the fully coupled mean climate between CAM3 and CAM4 are also more substantial than in forced sea surface temperature (SST) simulations.

Understanding extreme precipitation events in the current and future climate system is an important aspect of climate change for adaptation and mitigation purposes. The current study investigates extreme precipitation events over Madison, Wisconsin, during the late twentieth and late twenty-first centuries using 18 coupled ocean–atmosphere general circulation models that participated in the Coupled Model Intercomparison Project (CMIP3). An increase of ~10% is found in the multimodel average of annual precipitation received in Madison by the end of the twenty-first century, with the largest increases projected to occur during winter [December–February (DJF)] and spring [March–May (MAM)]. It is also found that the observed seasonal cycle of precipitation in Madison is not accurately captured by the models. The multimodel average shows a strong seasonal peak in May, whereas observations peak during midsummer. Model simulations also do not accurately capture the annual cycle of extreme precipitation events in Madison, which also peak in summer. Instead, the timing of model-simulated extreme events exhibits a bimodal distribution that peaks during spring and fall. However, spatial composites of average daily precipitation simulated by GCMs during Madison’s wettest 1% of precipitation events during the twentieth century strongly resemble the spatial pattern produced in observations. The role of specific humidity and vertically integrated moisture flux convergence (MFC) during extreme precipitation events in Madison is investigated in twentieth- and twenty-first-century simulations. Spatial composites of MFC during the wettest 1% of days during the twentieth-century simulations agree well with results from the North American Regional Reanalysis dataset (NARR), suggesting that synoptic-scale dynamics are vital to extreme precipitation events.

The authors summarize the twenty-first-century Arctic climate simulated by NCAR’s Community Climate System Model, version 4 (CCSM4). Under a strong radiative forcing scenario, the model simulates a much warmer, wetter, cloudier, and stormier Arctic climate with considerably less sea ice and a fresher Arctic Ocean. The high correlation among the variables composing these changes—temperature, precipitation, cloudiness, sea level pressure (SLP), and ice concentration—suggests that their close coupling collectively represents a fingerprint of Arctic climate change. Although the projected changes in CCSM4 are generally consistent with those in other GCMs, several noteworthy features are identified. Despite more global warming in CCSM4, Arctic changes are generally less than under comparable greenhouse forcing in CCSM3, as represented by Arctic amplification (16% weaker) and the date of a seasonally ice-free Arctic Ocean (20 years later). Autumn is the season of the most pronounced Arctic climate change among all the primary variables. The changes are very similar across the five ensemble members, although SLP displays the largest internal variability. The SLP response exhibits a significant trend toward stronger extreme Arctic cyclones, implying greater wave activity that would promote coastal erosion. Based on a commonly used definition of the Arctic (the area encompassing the 10°C July air temperature isotherm), the region shrinks by about 40% during the twenty-first century, in conjunction with a nearly 10-K warming trend poleward of 70°N. Despite this pronounced long-term warming, CCSM4 simulates a hiatus in the secular Arctic climate trends during a decade-long stretch in the 2040s and to a lesser extent in the 2090s. These pauses occur despite averaging over five ensemble members and are remarkable because they happen under the most extreme greenhouse-forcing scenario and in the most climatically sensitive region of the world.

This study diagnoses the changes in Arctic clouds simulated by the Community Climate System Model version 3 (CCSM3) in a transient 2 × CO2 simulation. Four experiments—one fully coupled and three with prescribed SSTs and/or sea ice cover—are used to identify the mechanisms responsible for the projected cloud changes. The target simulation uses a T42 version of the CCSM3, in which the atmosphere is coupled to a dynamical ocean with mobile sea ice. This simulation is approximated by a T42 atmosphere-only integration using CCSM3’s atmospheric component [the Community Atmosphere Model version 3 (CAM3)] forced at its lower boundary with the changes in both SSTs and sea ice concentration from CCSM3’s 2 × CO2 run. The authors decompose the combined effect of the higher SSTs and reduced sea ice concentration on the Arctic cloud response in this experiment by running two additional CAM3 simulations: one forced with modern SSTs and the projected sea ice cover changes in CCSM3 and the other forced with modern sea ice coverage and the projected changes in SSTs in CCSM3. The results suggest that future increases in Arctic cloudiness simulated by CCSM3 are mostly attributable to two separate processes. Low cloud gains are primarily initiated locally by enhanced evaporation within the Arctic due to reduced sea ice, whereas cloud increases at middle and high levels are mostly driven remotely via greater meridional moisture transport from lower latitudes in a more humid global atmosphere. The enhanced low cloudiness attributable to sea ice loss causes large increases in cloud radiative forcing during the coldest months and therefore promotes even greater surface warming. Because CCSM3’s Arctic cloud response to greenhouse forcing is similar to other GCMs, the driving mechanisms identified here may be applicable to other models and could help to advance our understanding of likely changes in the vertical structure of polar clouds.

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates? (2) Are there significant regional differences in accuracy among data sets? (3) How accurate are their mean values compared with extremes? (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

Rising levels of carbon dioxide since the preindustrial era have likely contributed to an observed warming of the global surface, and observations show global greening and an expansion of boreal forests. This study reproduces observed climate and vegetation trends associated with rising CO2 using a fully coupled atmosphere–ocean–land surface GCM with dynamic vegetation and decomposes the effects into physiological and radiative components. The simulated warming trend, strongest at high latitudes, was dominated by the radiative effect, although the physiological effect of CO2 on vegetation (CO2 fertilization) contributed to significant wintertime warming over northern Europe and central and eastern Asia. The net global greening of the model was primarily due to the physiological effect of increasing CO2, while the radiative and physiological effects combined to produce a poleward expansion of the boreal forests. Observed and simulated trends in tree ring width are consistent with the enhancement of vegetation growth by the physiological effect of rising CO2.

According to a new hypothesis, greenhouse-gas concentrations in the atmosphere should have fallen throughout the last several thousand years and caused a significant cooling of Earth's climate, but early anthropogenic emissions of carbon dioxide and methane kept temperatures relatively warm. A further prediction is that ice should have begun accumulating in northeast Canada several thousand years ago. We carry out a preliminary test of this hypothesis by reducing atmospheric CO2 and CH4 concentrations to their estimated ‘natural’ levels in an experiment with the GENESIS climate model. In the absence of anthropogenic contributions, global climate is almost 2 °C cooler than today and roughly one third of the way toward full-glacial temperatures. The hypothesis of an overdue glaciation is confirmed, but at a small scale: parts of Baffin Island retain snow cover year-round, and snow cover persists on high terrain in Labrador for 11 months of the year.

The primary goals of this project are to assess the ability of the current generation of global climate models (GCMs) to simulate extreme Arctic cyclones, and to identify changes in the characteristics of these storms caused by greenhouse-forced climate change to the present. These goals are being addressed through the following questions. First, how realistically does the widely used CCSM4 GCM simulate the observed characteristics of extreme Arctic cyclones, and how sensitive are these events to the horizontal resolution of the model? Second, do other GCMs generate such storms and, if so, are there any common characteristics among models that successfully do so? Third, does the preferred location of these systems and their impact shift as the cyclogenetic baroclinic zone induced by the sea ice margin migrates poleward with time? Fourth, what do these state-of-the-art climate models suggest about changes in the frequency vs. intensity of extreme Arctic cyclones? I am targeting these objectives through a retrospective analysis of the transient 20th century simulations (spanning years 1850-2005) among the GCMs participating in the latest Coupled Model Intercomparison Project (CMIP5). The output from various models is becoming available and eventually should include around 20 GCMs with widely varying resolutions. These simulations will be compared with a new atmospheric reanalysis data set covering almost this entire period (1871-2008) from NOAA's Earth System Research Laboratory: The 20th Century Reanalysis, Version 2. This data set is described in detail by Compo et al. (2010) and provides various atmospheric fields, including sea level pressure, on daily and sub-daily time scales at 2 degrees horizontal resolution. All of this work is being conducted by the PI.

A coupled atmosphere–mixed layer ocean GCM (GENESIS2) is forced with altered orbital boundary conditions for paleoclimates warmer than modern (6 kyr BP) and colder than modern (115 kyr BP) in the high-latitude Northern Hemisphere. A pair of experiments is run for each paleoclimate, one with sea-ice dynamics and one without, to determine the climatic effect of ice motion and to estimate the climatic changes at these times. At 6 kyr BP the central Arctic ice pack thins by about 0.5 m and the atmosphere warms by 0.7 K in the experiment with dynamic ice. At 115 kyr BP the central Arctic sea ice in the dynamical version thickens by 2–3 m, accompanied by a 2 K cooling. The magnitude of these mean-annual simulated changes is smaller than that implied by paleoenvironmental evidence, suggesting that changes in other earth system components are needed to produce realistic simulations. Contrary to previous simulations without atmospheric feedbacks, the sign of the dynamic sea-ice feedback is complicated and...

A one-dimensional (1-D), thermodynamic sea-ice model with parameterized ice dynamics is coupled to a mixed-layer ocean model and driven with prescribed atmospheric forcings for the central Arctic. The model is used to calculate the sensitivity of the ice pack to various parameterizations that have traditionally been neglected or considered only implicitly in large-scale sea-ice models. The model includes melt ponds, leads (with summertime stratification), an ice-export term, a stability-dependent air–sea heat-exchange coefficient, a prognostic ocean–ice heat exchange, a crude ice-thickness distribution, and a sophisticated albedo parameterization.The ice pack is sensitive to the partitioning of solar energy between lateral melting and mixed-layer warming, with the most realistic simulations occurring when the heat is nearly evenly divided between these two processes. Conversely, ice thickness and coverage are fairly insensitive to the amount of lateral mixing within the upper ocean, vertical mixing within leads, and to the partitioning of mixed-layer heat content between warming the water and melting the ice bottom. The ice concentration during summer is strongly dependent on the assumed ice-thickness distribution: the amount of open water during summer is less than half the size of the empirically based distribution used here, compared with one in which ice floes are distributed uniformly across a range of thicknesses.

The sensitivity of lake ice phenology to climatic variations is tested using a numerical lake-ice model (LIMNOS). The model simulates the evolution of ice and snow cover by time-integrating equations of vertical heat conduction through ice and snow. The required input variables are mean lake depth, air temperature and moisture, wind speed, solar radiation, snowfall, and cloudiness. The model simulates the ice-on and ice-off dates of three southern Wisconsin lakes to within 1 week of their historical averages, despite large differences in mean depth. Using hourly meteorological data from 196 l-l 990 as inputs, LIMNOS simulates the annual ice-on and ice-off dates of Lake Mendota with a median absolute error of only 2 d and 4 d, respectively. The atmospheric variables are altered to determine the sensitivity of Lake Mendota ice phenology to climate change. The simulated ice-off date shows stronger sensitivity than the ice-on date to air temperature changes, and the sensitivity of both dates is greater for climatic warmings than toolings. Increased snowfall causes a monotonic delay in the breakup date, whereas decreased snowfall nonlinearly hastens ice decay.

Projected changes in the relative abundance and timing of autumn-winter migration are assessed for seven dabbling duck species across the Mississippi and Atlantic Flyways for the mid- and late 21st century. Species-specific observed relationships are established between cumulative weather severity in autumn-winter and duck population rate of change. Dynamically downscaled projections of weather severity are developed using a high-resolution regional climate model, interactively coupled to a one-dimensional lake model to represent the Great Lakes and associated lake-effect snowfall. Based on the observed relationships and downscaled climate projections of rising air temperatures and reduced snow cover, delayed autumn-winter migration is expected for all species, with the least delays for the Northern Pintail and the greatest delays for the Mallard. Indeed, the Mallard, the most common and widespread duck in North America, may overwinter in the Great Lakes region by the late 21st century. This highlights the importance of protecting and restoring wetlands across the mid-latitudes of North America, including the Great Lakes Basin, because dabbling ducks are likely to spend more time there, which would impact existing wetlands through increased foraging pressure. Furthermore, inconsistency in the timing and intensity of the traditional autumn-winter migration of dabbling ducks in the Mississippi and Atlantic Flyways could have social and economic consequences to communities to the south, where hunting and birdwatching would be affected.

The thermodynamic sea ice code in a coupled atmosphere-mixed layer ocean GCM has been altered to allow the presence of open water within an ice pack (leads) and a prescribed turbulent oceanic heat flux at the ice bottom. Two experiments with the GCM are then performed: one with leads included and one without. A comparison between the two model runs is presented, in addition to a comparison between observations and the simulation with leads. Selected sea ice and atmospheric variables in the high-latitude Northern Hemisphere are analyzed to assess the sensitivity of these climatic components to the presence of leads and to identify feedback mechanisms that are introduced by leads. The inclusion of leads causes Northern Hemispheric sea ice concentration to decrease in every season, with year-round statistically significant reductions at the highest latitude band (81°N). Using the improved sea ice code, the model's simulation of sea ice concentration in the central Arctic is consistent with observati...