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Abstract Cloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof‐of‐concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere‐only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present‐day runs using multiple satellite climatologies and by comparing simulated δ 18 O of precipitation (δ 18 O p ), known to be sensitive to parameterization schemes, with a global database of speleothem δ 18 O records covering the Last Glacial Maximum (LGM), mid‐Holocene (MH) and pre‐industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ 18 O p values across all time periods, along with LGM and MH δ 18 O p anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non‐stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations. , Plain Language Summary Equilibrium climate sensitivity (ECS) is a key climate metric that quantifies the rise in global mean surface temperature in response to doubling of atmospheric CO 2 . Changes in hydroclimate, temperature extremes, and other aspects of future climate projections are closely tied to a model's ECS. For decades, ECS range has remained wide despite improvements from using multiple lines of evidence. One persistent source of this spread is related to cloud and convective processes, which occur at scales too small to be explicitly resolved, and thus require parameterizations to be represented in climate models. These parameterizations directly influence water isotopes by modulating simulated clouds and atmospheric circulation, and thus can be used to constrain model processes and identify model biases. In this work, we demonstrated that paleoclimate simulations are more parameter sensitive than the modern, highlighting the potential of past climates in discriminating cloud and convective parameterizations. Using satellite‐ and proxy‐model comparisons, we identified the top performing parameterizations which differ for each time period likely due to varying cloud feedbacks under diverse climatic forcing. Overall, our results provide a framework for fine‐tuning model representations using combined paleoclimate and satellite data, offering a unique opportunity to assess model uncertainties over a broader range of climate variability. , Key Points Paleoclimate relative to modern are more parameter sensitive, allowing for an assessment of uncertainties over a variety of climate forcing Certain simulations reproduced the δ 18 O of precipitation from paleoclimate proxies better than the default parameterization No single set of parameters works well in all climate states likely due to varying boundary conditions influencing cloud feedbacks

Abstract The Canadian Rockies are a triple-continental divide, whose high mountains are drained by major snow-fed and rain-fed rivers flowing to the Pacific, Atlantic, and Arctic Oceans. The objective of the April–June 2019 Storms and Precipitation Across the continental Divide Experiment (SPADE) was to determine the atmospheric processes producing precipitation on the eastern and western sides of the Canadian Rockies during springtime, a period when upslope events of variable phase dominate precipitation on the eastern slopes. To do so, three observing sites across the divide were instrumented with advanced meteorological sensors. During the 13 observed events, the western side recorded only 25% of the eastern side’s precipitation accumulation, rainfall occurred rather than snowfall, and skies were mainly clear. Moisture sources and amounts varied markedly between events. An atmospheric river landfall in California led to moisture flowing persistently northward and producing the longest duration of precipitation on both sides of the divide. Moisture from the continental interior always produced precipitation on the eastern side but only in specific conditions on the western side. Mainly slow-falling ice crystals, sometimes rimed, formed at higher elevations on the eastern side (>3 km MSL), were lifted, and subsequently drifted westward over the divide during nonconvective storms to produce rain at the surface on the western side. Overall, precipitation generally crossed the divide in the Canadian Rockies during specific spring-storm atmospheric conditions although amounts at the surface varied with elevation, condensate type, and local and large-scale flow fields.

Abstract Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.

Abstract In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation‐phase partitioning methods (PPMs) that use near‐surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric‐based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5‐km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric‐based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post‐processing algorithm determining the snow level and the associated precipitation phase. Two ground‐based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet‐bulb temperature. Compared to the upper benchmark, the snow‐level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post‐processed snow level. The limitations to drive snowpack models at slope scale are also discussed. , Plain Language Summary The partitioning of precipitation between rainfall and snowfall is a crucial component of the evolution of the snowpack in mountains. Most snowpack models use the air temperature and humidity near the surface to derive the precipitation phase. However, the phase at the surface is strongly influenced by processes such as melting and refreezing of falling hydrometeors that occur above the surface. Atmospheric models simulate these processes and the corresponding phase at the surface. However, snowpack models rarely use this information. In this study, we considered two estimates of precipitation phase from an atmospheric model and tested them with a physically‐based snow model over the mountains of southwestern Canada and northwestern United States. The results were compared with traditional approaches using the air temperature and humidity near the surface to derive the precipitation phase. Our results showed that the precipitation phase associated with the snow level obtained from the atmospheric model improved snowfall estimate and snowpack prediction compared to the traditional approaches. In contrast, the cloud/precipitation scheme of the atmospheric model decreased performance in phase estimate and snow simulations due to missing physical processes. Our study highlights that snowpack predictions in the mountains can be improved if valuable information is obtained from atmospheric models. , Key Points Estimates of precipitation phase from an atmospheric model were used to drive snow simulations with a detailed snowpack model Snowfall prediction and snowpack modeling are improved by using the snow level from post‐processing of the atmospheric model Direct precipitation phase from the microphysics scheme does not improve snow simulations compared to simpler rain‐snow partitioning schemes

Abstract Aim Compared with gradual climate change, extreme climatic events have more direct and dramatic impacts on vegetation growth. However, the influence of climate extremes on important phenological periods, such as the end of the growing season (EOS), remains unclear. Here, we investigate the temporal trends of EOS across different biomes and quantify the response of EOS to multiple climate extreme indices (CEIs). Location Northern middle and high latitudes. Time period 2000–2020. Major taxa studied Plants. Methods Three phenology extraction methods were used to compute EOS from satellite, FLUXNET and Pan European Phenology Project PEP725 phenological datasets. Different stress states of cold, hot, dry and wet extremes were represented by 12 CEIs. Partial correlation and ridge regression analysis were used to quantify the response of EOS to climate extremes across latitudinal and biome scales. Results Our study showed a delayed EOS in boreal biomes, but a significantly advanced EOS in temperate biomes. The advanced EOS induced by cold stress was observed for c . 80% of the vegetated pixels. The warm‐related CEIs delayed the EOS in high latitudes, and the delayed effect weakened or even reversed with decreasing latitude. In contrast, EOS exhibited opposite response patterns to dry days and wet‐related CEIs. Overall, EOS exhibited higher sensitivity to extreme temperature in boreal biomes than in temperate biomes. Specifically, continuous drought and high heat stress induced an earlier EOS in some temperate forest biomes, whereas moderate heat stress delayed the EOS in most study biomes. In contrast, EOS was not sensitive to extreme drought in water‐restricted biomes. Main conclusions EOS exhibited divergent responses to various climate extremes with different intensities and frequencies. Moreover, the response of EOS to extreme climate stress was dependent on the biome and latitude. These findings emphasize the importance of incorporating the divergent extreme climate effects into vegetation phenological models and Earth system models.

Abstract Climate changes over the past two millennia in the central part of the Gulf of St. Lawrence are documented in this paper with the aim of determining and understanding the natural climate variability and the impact of anthropogenic forcing at a regional scale. The palynological content (dinocysts, pollen, and spores) of the composite marine sediment core MSM46-03 collected in the Laurentian Channel was used to reconstruct oceanographic and climatic changes with a multidecadal temporal resolution. Sea-surface conditions, including summer salinity and temperature, sea-ice cover, and primary productivity, were reconstructed from dinocyst assemblages. Results revealed a remarkable cooling trend of about 4°C after 1230 cal yr BP (720 CE) and a culmination with a cold pulse dated to 170–40 cal yr BP (1780–1910 CE), which likely corresponds to the regional signal of the Little Ice Age. This cold interval was followed by a rapid warming of about 3°C. In the pollen assemblages, the decrease of Pinus abundance over the past 1700 yr suggests changes in wind regimes, likely resulting from increased southerly incursions of cold and dry Arctic air masses into southeastern Canada.

Although rice paddy fields are one of the world’s largest anthropogenic sources of methane CH4, the budget of ecosystem CH4 and its’ controls in rice paddies remain unclear. Here, we analyze seasonal dynamics of direct ecosystem-scale measurements of CH4 flux in a rice-wheat rotation agroecosystem over 3 consecutive years. Results showed that the averaged CO2 uptakes and CH4 emissions in rice seasons were 2.2 and 20.9 folds of the wheat seasons, respectively. In sum, the wheat-rice rotation agroecosystem acted as a large net C sink (averaged 460.79 g C m−2) and a GHG (averaged 174.38 g CO2eq m−2) source except for a GHG sink in one year (2016) with a very high rice seeding density. While the linear correlation between daily CH4 fluxes and gross ecosystem productivity (GEP) was not significant for the whole rice season, daily CH4 fluxes were significantly correlated to daily GEP both before (R2: 0.52–0.83) and after the mid-season drainage (R2: 0.71–0.79). Furthermore, the F partial test showed that GEP was much greater than that of any other variable including soil temperature for the rice season in each year. Meanwhile, the parameters of the best-fit functions between daily CH4 fluxes and GEP shifted between rice growth stages. This study highlights that GEP is a good predictor of daily CH4 fluxes in rice paddies.

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km2 by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.

Abstract The importance of resolving mesoscale air‐sea interactions to represent cyclones impacting the East Coast of Australia, the so‐called East Coast Lows (ECLs), is investigated using the Australian Regional Coupled Model based on NEMO‐OASIS‐WRF (NOW) at resolution. The fully coupled model is shown to be capable of reproducing correctly relevant features such as the seasonality, spatial distribution and intensity of ECLs while it partially resolves mesoscale processes, such as air‐sea feedbacks over ocean eddies and fronts. The mesoscale thermal feedback (TFB) and the current feedback (CFB) are shown to influence the intensity of northern ECLs (north of ), with the TFB modulating the pre‐storm sea surface temperature (SST) by shifting ECL locations eastwards and the CFB modulating the wind stress. By fully uncoupling the atmospheric model of NOW, the intensity of northern ECLs is increased due to the absence of the cold wake that provides a negative feedback to the cyclone. The number of ECLs might also be affected by the air‐sea feedbacks but large interannual variability hampers significant results with short‐term simulations. The TFB and CFB modify the climatology of SST (mean and variability) but no direct link is found between these changes and those noticed in ECL properties. These results show that the representation of ECLs, mainly north of , depend on how air‐sea feedbacks are simulated. This is particularly important for atmospheric downscaling of climate projections as small‐scale SST interactions and the effects of ocean currents are not accounted for. , Plain Language Summary Air‐sea interactions occur at a variety of spatial scales, including those of the size of ocean eddies. Such interactions are partially resolved in the Australian Regional Coupled Model used to simulate the cyclones impacting the East Coast of Australia, the so‐called East Coast Lows (ECLs). The effect of different feedbacks between the ocean and the atmosphere, including those due to mechanical and thermal exchanges over ocean eddies, are tested on the properties of ECLs. Significant effects are found on the intensity of ECLs north of , with also potential effects on the number of ECLs. The air‐sea feedbacks modify the climatology of sea surface temperature, with no direct link to ECL changes. Such experiments eventually demonstrate that small‐scale air‐sea feedbacks may matter for representing current Australian climate and its change in the future. , Key Points High‐resolution regional coupled modeling can simulate key features of East Australian cyclones Cyclone intensity is sensitive to mechanical and thermal air‐sea feedbacks at mesoscales Coupled and atmosphere‐only models mainly differ in simulating cyclone properties north of

A trait-based approach is an effective way to quantify plant adaptation strategies in response to changing environments. Single trait variations have been well depicted before; however, multi-trait covariations and their roles in shaping plant adaptation strategies along aridity gradients remain unclear. The purpose of this study was to reveal multi-trait covariation characteristics, their controls and their relevance to plant adaptation strategies. Using eight relevant plant functional traits and multivariate statistical approaches, we found the following: (1) the eight studied traits show evident covariation characteristics and could be grouped into four functional dimensions linked to plant strategies, namely energy balance, resource acquisition, resource investment and water use efficiency; (2) leaf area (LA) together with traits related to the leaf economic spectrum, including leaf nitrogen content per area (Narea), leaf nitrogen per mass (Nmass) and leaf dry mass per area (LMA), covaried along the aridity gradient (represented by the moisture index, MI) and dominated the trait–environmental change axis; (3) together, climate, soil and family can explain 50.4% of trait covariations; thus, vegetation succession along the aridity gradient cannot be neglected in trait covariations. Our findings provide novel perspectives toward a better understanding of plant adaptations to arid conditions and serve as a reference for vegetation restoration and management programs in arid regions.

Abstract. Starphotometry, the night-time counterpart of sunphotometry, has not yet achieved the commonly sought observational error level of 1 %: a spectral optical depth (OD) error level of 0.01. In order to address this issue, we investigate a large variety of systematic (absolute) uncertainty sources. The bright-star catalogue of extraterrestrial references is noted as a major source of errors with an attendant recommendation that its accuracy, particularly its spectral photometric variability, be significantly improved. The small field of view (FOV) employed in starphotometry ensures that it, unlike sun- or moonphotometry, is only weakly dependent on the intrinsic and artificial OD reduction induced by scattering into the FOV by optically thin clouds. A FOV of 45 arcsec (arcseconds) was found to be the best trade-off for minimizing such forward-scattering errors concurrently with flux loss through vignetting. The importance of monitoring the sky background and using interpolation techniques to avoid spikes and to compensate for measurement delay was underscored. A set of 20 channels was identified to mitigate contamination errors associated with stellar and terrestrial atmospheric gas absorptions, as well as aurora and airglow emissions. We also note that observations made with starphotometers similar to our High Arctic instrument should be made at high angular elevations (i.e. at air masses less than 5). We noted the significant effects of snow crystal deposition on the starphotometer optics, how pseudo OD increases associated with this type of contamination could be detected, and how proactive techniques could be employed to avoid their occurrence in the first place. If all of these recommendations are followed, one may aspire to achieve component errors that are well below 0.01: in the process, one may attain a total 0.01 OD target error.

Urbanization can induce environmental changes such as the urban heat island effect, which in turn influence the terrestrial ecosystem. However, the effect of urbanization on the phenology of subtropical vegetation remains relatively unexplored. This study analyzed the changing trend of vegetation photosynthetic phenology in Dongting Lake basin, China, and its response to urbanization using nighttime light and chlorophyll fluorescence datasets. Our results indicated the start of the growing season (SOS) of vegetation in the study area was significantly advanced by 0.70 days per year, whereas the end of the growing season (EOS) was delayed by 0.24 days per year during 2000–2017. We found that urbanization promoted the SOS advance and EOS delay. With increasing urbanization intensity, the sensitivity of SOS to urbanization firstly increased then decreased, while the sensitivity of EOS to urbanization decreased with urbanization intensity. The climate sensitivity of vegetation phenology varied with urbanization intensity; urbanization induced an earlier SOS by increasing preseason minimum temperatures and a later EOS by increasing preseason precipitation. These findings improve our understanding of the vegetation phenology response to urbanization in subtropical regions and highlight the need to integrate human activities into future vegetation phenology models.