This study focuses on the projections and time of emergence (TOE) for temperature extremes over Australian regions in the phase 6 of Coupled Model Intercomparison Project (CMIP6) models. The model outputs are based on the Shared Socioeconomic Pathways (SSPs) from the Tier 1 experiments (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) in the Scenario Model Intercomparison Project (ScenarioMIP), which is compared with the Representative Concentration Pathways (RCPs) in CMIP5 (i.e., RCP2.6, RCP4.5 and RCP8.5). Furthermore, two large ensembles (LEs) in CMIP6 are used to investigate the effects of internal variability on the projected changes and TOE. As shown in the temporal evolution and spatial distribution, the strongest warming levels are projected under the highest future scenario and the changes for some extremes follow a “warm-get-warmer” pattern over Australia. Over subregions, tropical Australia usually shows the highest warming. Compared to the RCPs in CMIP5, the multi-model medians in SSPs are higher for some indices and commonly exhibit wider spreads, likely related to the different forcings and higher climate sensitivity in a subset of the CMIP6 models. Based on a signal-to-noise framework, we confirm that the emergence patterns differ greatly for different extreme indices and the large uncertainty in TOE can result from the inter-model ranges of both signal and noise, for which internal variability contributes to the determination of the signal. We further demonstrate that the internally-generated variations influence the noise. Our findings can provide useful information for mitigation strategies and adaptation planning over Australia.

The Indian Ocean has warmed rapidly over the last half of the 20th century, with widespread effects on regional weather, and global climate. Determining the causes of the observed warming is challenging due to the lack of a long instrumental record of interior ocean temperature, leaving uncertainty around the active physical mechanisms and the role of decadal variability. Here we utilize unique temperature observations from three historical German oceanographic expeditions of the late 19th and early 20th centuries: SMS Gazelle (1874–1876), Valdivia (1898–1899), and SMS Planet (1906–1907). These observations reveal a mean 20th century ocean warming that extends over the upper 750 m, and a spatial pattern of subsurface warming and cooling consistent with a 1°–2° southward shift of the ocean gyres. These interior changes occurred largely over the last half of the 20th century, providing observational evidence for the acceleration of a multidecadal trend in subsurface Indian Ocean temperature.

The impacts of global change especially the recent climate-related extremes such as floods and droughts reveal significant vulnerability and exposure of freshwater ecosystems and related human systems to current climate variability. However, the effects of the extreme drought in the Okavango Delta system are not well understood and documented. Therefore, the objective of this use case was to apply the products from Digital Earth Africa namely: the Water Observation from Space (WOfS) derived from Landsat, vegetation cover baseline derived from Sentinel 2 data; and data from the meteorological agencies such as rainfall and measured river discharge data to evaluate the effects of drought in the Okavango Delta wetland system in relation to its upstream areas in Angola. In particular, we used the 2019 drought as a case study to assess inundation extent and vegetation cover dynamics with an emphasis on floodplain and dryland vegetation. Our preliminary results reveal that the Okavango Delta permanent marshes are resilient to drought, whereas seasonal floodplains are susceptible to drought. Further, we discovered that the geospatial location of floodplains has a direct effect on the timing of desiccation, with the western tributaries that flow into Lake Ngami and Thamalakane River being the last to dry out due to drought. In addition, we found that the drought phenomenon in the Cubango-Okavango River Basin region started earlier than 2019 spanning over a period of 5years; with 2018 as the year when the wetland system reached a minimum threshold for a tipping point triggered by the 2019 drought. In addition, the results contribute to the development of large-scale drought risk information and products for the Cubango- Okavango River Basin with a major focus in the Okavango Delta. Further, this use case provides recent baseline information on the effects of drought on vegetation cover and river flows in the Okavango Delta system at a landscape approach, which are essential elements for making informed science-based decisions on climate risks management and Sustainable Development Goals (SDGs) by relevant authorities in the Okavango Delta and the whole of Cubango-Okavango River Basin. In conclusion, this use case will be upscaled to other transboundary river basins in the Southern Africa Development Community.

Terrestrial primary productivity plays a pivotal role as a forcing factor of atmospheric CO2 and drives biospheric carbon dynamics. India is one of the largest GHGs emitters, yet less is understood in carbon cycling in terrestrial ecosystems. Here we explored the trend and magnitude of gross and net productivities of India for the last two decades (2000 – 2019) by integrating satellite observation from MODIS, remote sensing-based CASA model and twenty DGVMs from the TRENDY ensemble. Preliminary results exhibited a unimodal response across the data products with an overall positive trend and a declining decadal trend for 2010 – 2019. Alongside, the SPEI drought severity index across various ecological zones indicated India was more positively sensitive to wet span than the dry. We found that the ecosystems were drastically shifting their nature to C source with a positive trend in the productivities and were mediated by the changing climate. The analysis also revealed the increasing decadal amplitude of GPP by 0.0884 Pg C/Year, NBP by 0.0096 Pg C/Year, NEP by 0.0195 Pg C/Year, NPP by 0.0448 Pg C/Year and NEE by 0.0161 Pg C/Year. CASA underestimated the magnitudes but with the temporal synchronisation of the ensemble. Seasonal variability across the agro-ecological zones was more sensitive and was an offset for the declining productivities in the primaeval forests of India. The monsoon season contributed to the interannual variability of India. Higher uncertainty in productivities was observed in the high greening areas, whereas it contradicted NBP by reflecting a stable trend. Our results underscore the nature of C variability in the terrestrial ecosystems of India; and, they indicate that C release has reacted stronger than the C uptake, which was substantially inferred from NEE across the ecological zones.

Radon is a natural radioactive gas accounting for approximately one in ten lung cancer deaths, with substantially higher death rates in sub-Arctic communities. Radon transport is significantly reduced in permafrost, but permafrost is now thawing due to climate change. The effect of permafrost thawing on domestic radon exposure is unknown. Here we present results from radon transport modeling through soil, permafrost and model buildings either with basements or built on piles. We find that permafrost acts as an effective radon barrier, reducing radiation exposure to a tenth of the background level, while producing a ten-fold increase in the radon activity behind the barrier. When we model thawing of the permafrost barrier, we find no increase in radon to the background level for buildings on piles. However, for buildings with basements the radon increases to over one hundred times its initial value and can remain above the 200 Bq/m3 threshold for up to seven years depending on the depth of the permafrost and the speed of thawing. When thawing speed is taken into account, radiations remains higher than the threshold for all scenarios where 40% thawing occurs within 15 years. This new information suggests that a significant sub-Arctic population could be exposed to radon levels dangerous to health as a result of climate change thawing of permafrost, with implications for health provision, building codes and ventilation advice.

Water isotopes measured in Antarctic ice cores enable reconstruction at the first order of the past temperature variations. However, the seasonality of the precipitation and episodic events, including synoptic-scale disturbances, influence the isotopic signals recorded in ice cores. In this study, we adopted an isotope-enabled atmospheric general circulation model from 1981 to 2010 to investigate variations in climatic factors in δ18O of precipitation (δ18Op) at Dome Fuji, East Antarctica. The Southern Annular Mode (SAM), the primary mode of atmospheric circulation in the southern mid-high latitudes, significantly contributes to the isotope signals. Positive δ18Op anomalies, especially in the austral winter, are linked to the negative polarity of the SAM, which weakens westerly winds and increases the southward inflow of water vapor flux. Daily variations in temperature and δ18Op in Dome Fuji are significantly small in the austral summer, and their contribution to the annual signals is limited. The isotope signals driven by the SAM are a locational feature of Dome Fuji, related to the asymmetric component of the large-scale atmospheric pattern.

Minnesota is the U.S. state with the strongest winter warming in the contiguous United States. We performed regional climate projections at 10 km horizontal resolution using the WRF model forced by an ensemble of eight CMIP5 GCMs. The selected GCMs have previously been found to be in relatively good agreement with observations compared to other members of the CMIP5 model ensemble. Our projections suggest ongoing warming in all seasons, especially in winter, as well as shallower snow cover and fewer days with snow cover. On the other hand, we expect significant increases in spring and early summer heavy precipitation events. Our comparisons between different time slices and two different emission scenarios indicate a climate for the state of Minnesota at the end of the 21st century that is significantly different from what has been observed by the end of the 20th century. Winters and summers are expected to be up to 6oC and 4oC warmer, respectively, over northern and central Minnesota and spring precipitation may increase by more than 1 mm d-1 over northern Minnesota. Especially over the central part of the state, winter snow height is suggested to decrease by more than 0.5 meters and the number of days per year with snow height of more than 0.0254 meters (one inch) is expected to decrease by up to 60.

Earth’s tropical and subtropical rainbands, such as Intertropical Convergence Zones (ITCZs) and monsoons, are complex systems, governed by both large-scale constraints on the atmospheric general circulation and regional interactions with continents and orography, and coupled to the ocean. Monsoons have historically been considered as regional large-scale sea breeze circulations, driven by land-sea contrast. More recently, a perspective has emerged of a Global Monsoon, a global-scale solstitial mode that dominates the annual variation of tropical and subtropical precipitation. This results from the seasonal variation of the global tropical atmospheric overturning and migration of the associated convergence zone. Regional subsystems are embedded in this global monsoon, localized by surface boundary conditions. Parallel with this, much theoretical progress has been made on the fundamental dynamics of the seasonal Hadley cells and convergence zones via the use of hierarchical modeling approaches, including aquaplanets. Here we review the theoretical progress made, and explore the extent to which these advances can help synthesize theory with observations to better understand differing characteristics of regional monsoons and their responses to certain forcings. After summarizing the dynamical and energetic balances that distinguish an ITCZ from a monsoon, we show that this theoretical framework provides strong support for the migrating convergence zone picture and allows constraints on the circulation to be identified via the momentum and energy budgets. Limitations of current theories are discussed, including the need for a better understanding of the influence of zonal asymmetries and transients on the large-scale tropical circulation.

Over the last nearly five decades, optical remote sensing has played a key role in monitoring and quantifying global change, plant diversity, and vegetation functioning across Earth’s terrestrial biomes. As a key tool for researchers, land managers, and policy makers, optical remote sensing facilitates scaling, mapping, and characterizing surface properties over large areas and through time. In addition, steady technological improvements have led to transformational changes in our ability to understand ecosystem state and change, particularly through the expansion of high spectral resolution (i.e. spectroscopic) remote sensing platforms. Point and imaging spectroscopy systems have been used across a range of scales, vegetation types, and biomes to infer plant diversity, leaf traits, and ecosystem functioning. However, despite the acknowledged utility of spectroscopic systems, data availability has been limited to smaller geographic regions given a number of technical challenges, including issues related to data volume and limited spatial coverage by previous Earth Observing (EO) missions (i.e. Hyperion). The NASA Surface Biology and Geology (SBG) mission is designed to fill this gap in ecosystem monitoring. As part of the Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) and Modeling end-to-end traceability (MEET) SBG efforts, we used field, unoccupied aerial system (UAS), and airborne imagery (from NASA’s AVIRIS-NG plafrom) to evaluate the impacts of proposed and theoretical sensor instrument properties on the retrieval of vegetation reflectance across tundra, shrub, and treeline ecosystems in Alaska. Existing observations and open-source tools are used for the simulation of surface reflectance under a range of atmospheric conditions, vegetation types, and different sensor properties. We find that retrieval uncertainty is reduced across all surface types with increasing detector signal-to-noise (SNR) but also key differences across different plant types. Results were also strongly tied to sun-sensor geometry and atmospheric state. Through this exercise we highlight key outcomes to consider for the SBG mission to optimize surface reflectance retrieval in high latitudes that will help to minimize errors in down-stream algorithms, such as functional trait retrievals.

We explore the use of three advanced statistical and machine learning methods (a generalized linear model, random forest, and neural network) to predict the occurrence and rain rate distribution of three tropical rain types (deep convective, stratiform, and shallow convective) observed by the radar onboard the GPM satellite over the West Pacific at three-hourly, 0.5-degree resolution. Temperature and moisture profiles from MERRA-2 were used as predictors. All three methods perform reasonably well at predicting the occurrence and rain rate distribution of each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models.

Changes in the circulation of the Southern Ocean are known to have impacted global nutrient, heat, and carbon cycles during the glacial and interglacial periods of the late Pleistocene. Proxy-based records of these changes deserve continued scrutiny as the implications may be important for constraining future change. A record of authigenic uranium from the South Atlantic has been used to infer changes in deep-sea oxygenation and organic matter export over the past 0.5 million years. Since sedimentary uranium has the possible complication of remobilization, it is prudent to investigate the behavior of other redox-sensitive trace metals to confidently interpret temporal changes in oxygenation. Focusing here on the exceptionally long interglacial warm period, Marine Isotope Stage (MIS) 11, we found concurrent authigenic enrichments of uranium and rhenium throughout MIS 12 to 10, overall supporting prior interpretations of low-oxygen periods. However, there are differential responses of Re and U to oxygen changes and some evidence of small-scale Re remobilization, which may involve differences in molecular-level reduction mechanisms. Peaks in authigenic manganese intervening with peaks in Re and U indicate increases in porewater oxygenation which likely relate to increased Antarctic Bottom Water circulation at the onset of MIS11c and during the peak warmth of the interglacial around 400 ka.

Our current understanding of global mean near-surface (land and sea) air temperature (GMSAT) during the Cenozoic era relies on paleo-proxy estimates of deep-sea temperature combined with assumed relationships between global mean deep-sea temperature (GMDST), global mean sea-surface temperature (GMSST), and GMSAT. The validity of these assumptions is essential in our understanding of past climate states such as the Early Eocene Climate Optimum hothouse climate (EECO, 56–48 Ma). The EECO remains relevant today, because EECO-like CO2 levels are possible in the 22nd century under continued high CO2 emissions. We analyze the relationship between the three global temperature indicators for the EECO using 25 different millennia-long model simulations with varying CO2 levels from the Deep-Time Model Intercomparison Project (DeepMIP). The model simulations show limited spatial variability in deep-sea temperature, indicating that local temperature estimates can be regarded representative of GMDST. Linear regression analysis indicates that compared to GMSST, both GMDST and GMSAT respond more strongly to changes in atmospheric CO2 by factors of 1.18 and 1.17, respectively. Consequently, this model-based analysis validates the assumption that changes in GMDST can be used to estimate changes in GMSAT during the EECO. Paleo-proxies of GMDST, GMSST, and GMSAT during EECO show the best fit with model simulations having an atmospheric CO2 level of 1,680 ppm, which matches paleo-proxies of atmospheric CO2 during EECO. Similar analyses of other past climate states are needed to examine whether these results are robust throughout the Cenozoic, providing insight into the long-term future warming under various shared socioeconomic pathways.

Several bills moving through Congress are likely to provide significant funding for expanding research and results in climate change solutions (CCS). This is also a priority of the Biden-Harris Administration. The National Science Foundation (NSF) will be expected to distribute and manage much of this funding through its grant processes. Effective solutions require both a continuation and expansion of research on climate change–to understand and thus plan for potential impacts locally to globally and to continually assess solutions against a changing climate–and rapid adoption and implementation of this science with society at all levels. NSF asked AGU to convene its community to help provide guidance and recommendations for enabling significant and impactful CCS outcomes by 1 June. AGU was asked in particular to address the following: 1. Identify the biggest, more important interdisciplinary/convergent challenges in climate change that can be addressed in the next 2 to 3 years 2. Create 2-year and 3-year roadmaps to address the identified challenges. Indicate partnerships required to deliver on the promise. 3. Provide ideas on the creation of an aggressive outreach/communications plan to inform the public and decision makers on the critical importance of geoscience. 4. Identify information, training, and other resources needed to embed a culture of innovation, entrepreneurialism, and translational research in the geosciences. Given the short time frame for this report, AGU reached out to key leaders, including Council members, members of several committees, journal editors, early career scientists, and also included additional stakeholders from sectors relevant to CCS, including community leaders, planners and architects, business leaders, NGO representatives, and others. Participants were provided a form to submit ideas, and also invited to two workshops. The first was aimed at ideation around broad efforts and activities needed for impactful CCS; the second was aimed at in depth development of several broad efforts at scale. Overall, about 125 people participated; 78 responded to the survey, 82 attended the first workshop, and 28 attended the more-focused second workshop (see contributor list). This report provides a high-level summary of these inputs and recommendations, focusing on guiding principles and several ideas that received broader support at the workshops and post-workshop review. These guiding principles and ideas cover a range of activities and were viewed as having high importance for realizing impactful CCS at the scale of funding anticipated. These cover the major areas of the charge, including research and solutions, education, communication, and training. The participants and full list of ideas and suggestions are provided as an appendix. Many contributed directly to this report; the listed authors are the steering committee.

The future uncertainty and complexity of alternative socioeconomic and climatic scenarios challenge the model-based analysis of sustainable development. Obtaining robust insights requires a systematic processing of uncertainty and complexity not only in input assumptions, but also in the diversity of model structures that simulates the multisectoral dynamics of human and Earth system interactions. Here, we implement the global change scenarios, i.e., the Shared Socioeconomic Pathways and the Representative Concentration Pathways, in a feedback-rich, integrated assessment model of human-Earth system dynamics, called FeliX, to serve two aims: (1) to provide modellers with well-defined steps for the adoption of established scenarios in new integrated assessment models; (2) to explore the impacts of model uncertainty and its structural complexity on the projection of these scenarios for sustainable development. Our modelling shows internally consistent scenario storylines across sectors, yet with quantitatively different realisations of these scenarios compared to other integrated assessment models due to the new model’s structural complexity. The results highlight the importance of enumerating global change scenarios and their uncertainty exploration with a diversity of models of different input assumptions and structures to capture a wider variety of future possibilities and sustainability indicators.

The ferrimagnetic (FM) and antiferromagnetic (AFM) particles of iron oxides are considered pedogenic and climatic indicators due to their enrichment with comparable increasing in rainfall and temperature. However, the opposite changes in rainfall and temperature result in rapid change of relative humidity (RH), which could lead to their competition and transformation. We examined two soil sequences undergone contrary climate development on the eastern edge of the Tibetan Plateau. The dry and warm climate with low RH favors the coordinative enrichment of AFM hematite and FM particles, while the wet and cool climate with high RH mainly produces goethite but leads to competition between low content AFM hematite and FM particles. The outcome well interprets the changing relationship between color and magnetism in soils and sediments, and suggests that temperature is as important as precipitation in paleoclimate reconstruction based on iron oxides, especially during strong dry-wet cycles and climate pattern shifts.

Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.

It remains difficult to disentangle the relative influences of aerosols and greenhouse gases on regional surface temperature trends in the context of global climate change. To address this issue, we use a new collection of initial-condition large ensembles from the Community Earth System Model version 1 that are prescribed with different combinations of industrial aerosol and greenhouse gas forcing. To compare the climate response to these external forcings, we adopt an artificial neural network (ANN) architecture from previous work that predicts the year by training on maps of near-surface temperature. We then utilize layer-wise relevance propagation (LRP) to visualize the regional temperature signals that are important for the ANN’s prediction in each climate model experiment. To mask noise when extracting only the most robust climate patterns from LRP, we introduce a simple uncertainty metric that can be adopted to other explainable artificial intelligence (AI) problems. We find that the North Atlantic, Southern Ocean, and Southeast Asia are key regions of importance for the neural network to make its prediction, especially prior to the early-21st century. Notably, we also find that the ANN predictions based on maps of observations correlate higher to the actual year after training on the large ensemble experiment with industrial aerosols held fixed to 1920 levels. This work illustrates the sensitivity of regional temperature signals to changes in aerosol forcing in historical simulations. By using explainable AI methods, we have the opportunity to improve our understanding of (non)linear combinations of anthropogenic forcings in state-of-the-art global climate models.

Biological productivity in the ocean directly influences the partitioning of carbon between the atmosphere and ocean interior. Through this carbon cycle feedback, changing ocean productivity has long been hypothesized as a key pathway for modulating past atmospheric carbon dioxide levels and hence global climate. Because phytoplankton preferentially assimilate the light isotopes of carbon and the major nutrients nitrate and silicic acid, stable isotopes of carbon (C), nitrogen (N), and silicon (Si) in seawater and marine sediments can inform on ocean carbon and nutrient cycling, and by extension the relationship with biological productivity and global climate. Here we compile water column C, N, and Si stable isotopes from GEOTRACES-era data in four key ocean regions to review geochemical proxies of oceanic carbon and nutrient cycling based on the C, N, and Si isotopic composition of marine sediments. External sources and sinks as well as internal cycling (including assimilation, particulate matter export, and regeneration) are discussed as likely drivers of observed C, N, and Si isotope distributions in the ocean. The potential for C, N, and Si isotope measurements in sedimentary archives to record aspects of past ocean C and nutrient cycling is evaluated, along with key uncertainties and limitations associated with each proxy. Constraints on ocean C and nutrient cycling during late Quaternary glacial-interglacial cycles and over the Cenozoic are examined. This review highlights opportunities for future research using multielement stable isotope proxy applications and emphasizes the importance of such applications to reconstructing past changes in the oceans and climate system.

Effective radiative forcing (ERF) is evaluated in the ACCESS1.0 General Circulation Model (GCM) with fixed land and sea-surface-temperatures as well as sea-ice. The 4xCO2 ERF is 8.0 Wm-2. In contrast, a typical ERF experiment with only fixed sea-surface-temperatures (SST) and sea-ice gives rise to an ERF of only 7.0 Wm-2. This difference arises due to the influence of land warming in the commonly used fixed-SST ERF experimental design, which results in: (i) increased emission of longwave radiation to space from the land surface (-0.45 Wm-2) and troposphere (-0.90 Wm-2), (ii) reduced land snow-cover and albedo (+0.17 Wm-2), (iii) increased water-vapour (+0.49 Wm-2), and (iv) a cloud adjustment (-0.26 Wm-2) due to reduced stability and cloudiness over land (positive ERF) counteracted by increased lower tropospheric stability and marine cloudiness over oceans (negative ERF) . The sum of these radiative adjustments to land warming is to reduce the 4xCO2 ERF in fixed-SST experiments by ~1.0 Wm-2. CO2 stomatal effects are quantified and found to contribute just over half of the land warming effect and adjustments in the fixed-SST ERF experimental design in this model. The basic physical mechanisms in response to land warming are confirmed in a solar ERF experiment. We test various methods that have been proposed to account for land warming in fixed-SST ERFs against our GCM results and discuss their strengths and weaknesses.

Evaluation of the climatic water balance (CWB) – i.e. precipitation minus potential evapotranspiration – has strong potential as a tool for investigating patterns of variability and change in the water cycle since it estimates the (im)balance of atmospheric moisture near the land surface. Using observations from a middle-Himalaya weather station at Mukteshwar (29.474°N, 79.646°E, Uttarakhand state) in India, we demonstrate a CWB-based set of analytical procedures can robustly characterise local climate variability. Use of the CWB circumvents uncertainties in the soil water balance stemming from limited data on subsurface properties. We also focus on three key input variables used to calculate the CWB: precipitation, mean temperature and diurnal temperature range. We use local observations to evaluate the skill of gridded datasets –specifically meteorological reanalyses – in representing local conditions. Reanalysis estimates of Mukteshwar climate showed large absolute biases but accurately captured the timing and relative amplitude of the annual cycle of these three variables and the CWB. This suggests that the reanalyses can provide insight regarding climate processes in data-sparse regions, but caution is necessary if extracting absolute values. While the local observations at Mukteshwar show clear annual cycles and substantial interannual variability, results from investigation of their time-dependency were quite mixed. Pragmatically this implies that while “change is coming, variability is now.” If communities can adapt to the observed historical hydroclimate variability they will have built meaningful adaptive capacity to cope with on-going environmental change. This follows a ‘low regret’ approach advocated when facing a substantially uncertain future.

Previous work has established that warming is associated with an increase in dry static stability, a weakening of the tropical circulation, and a decrease in the convective mass flux. Using a set of idealized simulations with specified surface warming and super-parameterized convection, we find support for these previous conclusions. We use an energy and mass balance framework to develop a simple diagnostic that links the fractional area covered by the region of upward motion to the strength of the mean circulation. We demonstrate that the diagnostic works well for our idealized simulations, and use it to understand how changes in tropical ascent area and the strength of the mean circulation relate to changes in heating in the ascending and descending regions. We show that the decrease in the strength of the mean circulation can be explained by the relatively slow rate at which atmospheric radiative cooling intensifies with warming. In our simulations, decreases in tropical ascent area are balanced by increases in non-radiative heating in convective regions. Consistent with previous work, we find a warming-induced decrease in the mean convective mass flux. However, when we condition by the sign of the mean vertical motion, the warming-induced changes in the convective mass flux are non-monontonic and opposite between the ascending and descending regions.

In coral reef studies, mass spectrometry methods are widely applied to determine geochemical proxies in corals as a tool to evaluate seawater changes. As the coral grows, its skeleton forms annual bands similar to the growth rings found in trees. The density of the calcium carbonate skeletons changes as the water temperature, light, and nutrient conditions change. The elements stored within these bands can provide insight into the changing conditions of seawater over the entire lifetime of the coral, and serve as useful environmental records. Corals incorporate trace elements that can be precisely measured using high-resolution techniques, such as Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS). This analytical tool offers high levels of precision to determine the distribution of trace elements along the annual bands of coral skeletons. This approach can serve to monitor fixed-point time-series for water quality research, as well as large-scale observations in ocean science. Ultimately, this procedure can be applied to reconstruct past climate oscillation episodes and/or to quantify the impacts of marine pollution on coral reefs. The benefits of techno-scientific aspects of new and established mass spectrometry applications in coral reef research hold great promise that may continue to be improved in future studies. Given the current climate crisis, this issue requires accurate measurements to increase our understanding on the impacts that have become more frequent and intense.

The Distributed Hydrology Soil Vegetation Model (DHSVM) code was parallelized for distributed memory computers using the Global Arrays (GA) programming model. To analyze parallel performance, DHSVM was used to simulate the hydrology in two river basins of significant size located in the northwest continental United States and southwest Canada at 90~m resolution: the (1) Clearwater (25,000~km) and (2) Columbia (668,000~km) River basins. Meteorological forcing applied to both basins was dynamically down-scaled from a regional reanalysis using the Weather Research and Forecasting (WRF) model and read into DHSVM as 2D maps for each time step. Parallel code speedup was significant. Run times for 1-year simulations were reduced by an order of magnitude for both test basins. A maximum parallel speedup of 105 was attained with 480 processors while simulating the Columbia River basin. Speedup was limited by input-dominated tasks, particularly the input of meteorological forcing data.

Mesoscale convective systems (MCSs) have been identified as an important source of precipitation in the Tibetan Plateau (TP) region. However, the characteristics and structure of MCS-induced precipitation are not well understood. Infrared satellite imagery has been used for MCS tracking, but cirrus clouds or cold surfaces can cause misclassifications of MCS in mountain regions. We therefore combine brightness temperatures from IR imagery with satellite precipitation data from GPM and track MCSs over the TP, at the boundary of the TP (TPB) and in the surrounding lower-elevation plains (LE) between 2000 and 2019. We show that MCSs are less frequent over the TP than earlier studies have suggested and most MCSs over land occur over the Indo-Gangetic Plain (LE) and the south of the Himalayas (TPB). In the LE and TPB, MCSs have produced 10 % to 55 % of the total summer precipitation (10 % to 70 % of summer extreme precipitation), whereas MCSs over the TP account for only 1 % to 10 \% to the total summer precipitation (1 % to 30 % of the total summer extreme precipitation). Our results also show that MCSs that produce the largest amounts of convective precipitation are characterized by longevity and large extents rather than by high intensities. These are mainly located south of the TP, whereas smaller-scale convection makes a greater contribution to total and total extreme precipitation over the TP. These results highlight the importance of convective scale modeling to improve our understanding of precipitation dynamics over the TP.