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Soil carbon stocks in the tundra and underlying permafrost have become increasingly vulnerable to microbial decomposition due to climate change. The microbial responses to Arctic warming must be understood in order to predict the effects of future microbial activity on carbon balance in a warming Arctic.
To address the data needs for resilience and sustainability of building systems within ASHRAE, the NASA Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data on a long-term, global scale. Solar data from several NASA projects and meteorological data from NASA assimilation models have been reformatted and disseminated to the public via a user friendly web GIS-enabled based data portal (https://power.larc.nasa.gov) in selectable data formats, immediately amenable to key industry wide decision support tools such as EnergyPlus. Time series of potential climate conditions from downscaled climate simulations are being made available for similar statistics and formats. An emphasis is placed upon obtaining and utilizing time series data to conduct analysis of building system performance for current and planning building systems for energy savings and greenhouse gas emissions. This presentation has the learning following learning objectives for the ASHRAE community: 1) Learn how NASA POWER’s web service suite can provide supplementary solar and surface meteorological data parameters, including a discussion of uncertainty. 2) Learn how downscaled climate scenario information can be provided to evaluate potential future requirements. The presentation is part of a forum entitled, "Weather Data for Large-Scale Building Energy Modeling".
Abstract The response of tropical cyclone (TC) frequency to sea surface warming is uncertain in climate models. We hypothesize that one source of uncertainty is the anomalies of large-scale atmospheric radiation in response to climate change, and whose influence on TC frequency is investigated. Given two atmospheric models with opposite TC frequency responses to uniform sea surface warming, we interchange their atmospheric radiation anomalies in experiments with prescribed radiative heating rates. The largest model discrepancy occurs in the western North Pacific, where the TC frequency tends to increase with anomalous large-scale ascent caused by prescribed positive radiation anomalies, while the TC frequency tends to decrease with anomalous large-scale descent caused by prescribed negative radiation anomalies. The model spread in TC frequency response is approximated by the model spread in the frequency response of pre-TC vortices (seeds), which is explained by changes in the large-scale circulation using a downscaling formula known as the seed propensity index. We further generalize the index to predict the influence of large-scale radiation anomalies on TC seed frequency. The results show that model spread in TC and seed frequency response can be reduced when constraining the large-scale radiation anomalies. Significance Statement It is difficult to predict whether tropical cyclones will occur more or less frequently in the future and by how much. We show that tropical cyclone frequency is strongly influenced by the global pattern of heating and cooling due to radiation, a process that has been neglected in existing theories. Our theory improves understanding of how tropical cyclones respond to climate change, explaining why one model may predict a frequency increase while a different but equally realistic model may predict a frequency decrease. One reason for the difficulty in predicting tropical cyclone frequency is found to be the difficulty in predicting how global cloud distribution will change in the future.
Lowland tropical forest soils are relatively N rich and are the largest global source of N 2 O (a powerful greenhouse gas) to the atmosphere. Despite the importance of tropical N cycling, there have been few direct measurements of N 2 (an inert gas that can serve as an alternate fate for N 2 O) in tropical soils, limiting our ability to characterize N budgets, manage soils to reduce N 2 O production, or predict the future role that N limitation to primary productivity will play in buffering against climate change. We collected soils from across macro‐ and micro‐topographic gradients that have previously been shown to differ in O 2 availability and trace gas emissions. We then incubated these soils under oxic and anoxic headspaces to explore the relative effect of soil location versus transient redox conditions. No matter where the soils came from, or what headspace O 2 was used in the incubation, N 2 emissions dominated the flux of N gas losses. In the macrotopography plots, production of N 2 and N 2 O were higher in low O 2 valleys than on more aerated ridges and slopes. In the microtopography plots, N 2 emissions from plots with lower mean soil O 2 (5%–10%) were greater than in plots with higher mean soil O 2 (10%–20%). We estimate an N gas flux of ∼37 kg N/ha/yr from this forest, 99% as N 2 . These results suggest that N 2 fluxes may have been systematically underestimated in these landscapes, and that the measurements we present call for a reevaluation of the N budgets in lowland tropical forest ecosystems.
Boreal forest ecosystems are predicted to experience more frequent summer droughts due to climate change, posing a threat to future forest health and carbon sequestration. Forestry is a regionally dominant land use where the managed secondary forests are typically even-aged forests with low structural and tree species diversity. It is not well known if managed secondary forests and unmanaged primary forests respond to drought differently in part because the location of primary, unmanaged, forests has remained largely unknown. Here we employed a unique map detailing over 300 primary forests in Sweden. We studied impacts of the 2018 nationwide drought by extracting and analyzing a high-resolution remote sensing vegetation index over the primary forests and over buffer zones around the primary forests representing secondary forests. We controlled for topographical variations linked to soil moisture, which was a strong determinant of drought responses, and analyzed Landsat-derived EVI2 anomalies during the drought year from a multiyear non-drought baseline. We found that primary forests were less affected by the drought compared to secondary forests. Our results indicate that forestry may exacerbate the impact of drought in a future climate with more frequent and extreme hydroclimatic events.
Abstract. The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January–March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies.
Satellite measurements are used to quantify the atmospheric greenhouse effect, defined here as the infrared radiation energy trapped by atmospheric gases and clouds. The greenhouse effect is found to increase significantly with sea surface temperature. The rate of increase gives compelling evidence for the positive feedback between surface temperature, water vapor and the greenhouse effect; the magnitude of the feedback is consistent with that predicted by climate models. This study demonstrates an effective method for directly monitoring, from space, future changes in the greenhouse effect.
A 2200 to 2600 year quasi-periodicity is present in atmospheric delta C-14 records after removal of long-term trends due to the geomagnetic dipole amplitude variation. This periodicity consists of both a long-term variation of the mean and a superposed, approximately recurring pattern of century-scale variations. The strongest of these latter variations occur near maxima of the approx. 2400 year delta C-14 cycles. The residual record can be modeled to first order as an amplitude modulation of a century-scale periodic forcing function by a approx. 2400 year periodic forcing function. During the last millennium, the largest century-scale variations (occurring near the most recent 2400 year delta C-14 maximum) are known to be mainly a consequence of the pronounced Maunder, Sporer, and Wolf solar activity minima, as verified by independent proxy solar activity records. Therefore, during this period, amplitude modulation has been occurring primarily in the sun and not in the terrestrial radiocarbon system. It is therefore inferred that the approx. 2400 year forcing function is mainly solar although some secondary terrestrial feedback into the delta C-14 record is likely. This conclusion has implications for the predictability of future pronounced solar activity minima and for the interpretation of certain minor Holocene climatic variations.
Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1∕8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.
As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.
As temperatures increase, the onset and severity of droughts is likely to become more intense. Improved tools for understanding, monitoring and predicting droughts will be a key component of 21st century climate adaption. The best drought monitoring systems will bring together accurate precipitation estimates with skillful climate and weather forecasts. Such systems combine the predictive power inherent in the current land surface state with the predictive power inherent in low frequency ocean-atmosphere dynamics. To this end, researchers at the Climate Hazards Group (CHG), in collaboration with partners at the USGS and NASA, have developed i) a long (1981-present) quasi-global (50degS-50degN, 180degW-180degE) high resolution (0.05deg) homogenous precipitation data set designed specifically for drought monitoring, ii) tools for understanding and predicting East African boreal spring droughts, and iii) an integrated land surface modeling (LSM) system that combines rainfall observations and predictions to provide effective drought early warning. This talk briefly describes these three components. Component 1: CHIRPS The Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), blends station data with geostationary satellite observations to provide global near real time daily, pentadal and monthly precipitation estimates. We describe the CHIRPS algorithm and compare CHIRPS and other estimates to validation data. The CHIRPS is shown to have high correlation, low systematic errors (bias) and low mean absolute errors. Component 2: Hybrid statistical-dynamic forecast strategies East African droughts have increased in frequency, but become more predictable as Indo- Pacific SST gradients and Walker circulation disruptions intensify. We describe hybrid statistical-dynamic forecast strategies that are far superior to the raw output of coupled forecast models. These forecasts can be translated into probabilities that can be used to generate bootstrapped ensembles describing future climate conditions. Component 3: Assimilation using LSMs CHIRPS rainfall observations (component 1) and bootstrapped forecast ensembles (component 2) can be combined using LSMs to predict soil moisture deficits. We evaluate the skill such a system in East Africa, and demonstrate results for 2013.
Physiological maintenance of ectotherms is largely dependent on temperature. Abrupt changes in environmental conditions can cause shifts in energy budgets, with higher temperatures requiring more energy be devoted to maintenance functions, making less energy available for other activities. As a group, freshwater turtles are of conservation concern; thus, it is important to understand their physiological responses to temperature shifts, particularly given current climate change predictions. Here, we quantified metabolic rates (MR) for three species of semi-aquatic turtles that vary in their degree of terrestriality and propensity to aestivate: chicken turtles (Deirochelys reticularia), eastern mud turtles (Kinosternon subrubrum), and eastern musk turtles (Sternotherus odoratus). Metabolic trials were performed using a flow-through respirometer at three environmentally-relevant temperatures: 25°C, 30°C, and 35°C. As expected, MRs of turtles were significantly and positively associated with increases in mass and temperature. Pairwise comparisons revealed that D. reticularia exhibited significantly higher oxygen consumption rates (VO 2 ) relative to K. subrubrum, which had significantly higher VO 2 than S. odoratus. The combination of higher metabolic rates, the tendency to terrestrially aestivate, and declining quantity of high quality thermal habitats may place some semi-aquatic turtles at a disadvantage when considering future climate scenarios. Our findings, when combined with knowledge of other ecological traits, may be a useful tool for predicting relative sensitivities of turtle species to predicted climate change, particularly those species inhabiting seasonal wetlands.
Regional droughts are common in North America, but pan-continental droughts extending across multiple regions, including the 2012 event, are rare relative to single-region events. Here, the tree-ring-derived North American Drought Atlas is used to investigate drought variability in four regions over the last millennium, focusing on pan-continental droughts. During the Medieval Climate Anomaly (MCA), the central plains (CP), Southwest (SW), and Southeast (SE) regions experienced drier conditions and increased occurrence of droughts and the Northwest (NW) experienced several extended pluvials. Enhanced MCA aridity in the SW and CP manifested as multidecadal megadroughts. Notably, megadroughts in these regions differed in their timing and persistence, suggesting that they represent regional events influenced by local dynamics rather than a unified, continental-scale phenomena. There is no trend in pan-continental drought occurrence, defined as synchronous droughts in three or more regions. SW, CP, and SE (SW+CP+SE) droughts are the most common, occurring in 12 percent of all years and peaking in prevalence during the twelfth and thirteenth centuries; patterns involving three other regions occur in about 8 percent of years. Positive values of the Southern Oscillation index (La Nina conditions) are linked to SW, CP, and SE (SW+CP+SE) droughts and SW, CP, and NW (SW+CP+NW) droughts, whereas CP, NW, and SE (CP+NW+SE) droughts are associated with positive values of the Pacific decadal oscillation and Atlantic multidecadal oscillation. While relatively rare, pan-continental droughts are present in the paleo record and are linked to defined modes of climate variability, implying the potential for seasonal predictability. Assuming stable drought teleconnections, these events will remain an important feature of future North American hydroclimate, possibly increasing in their severity in step with other expected hydroclimate responses to increased greenhouse gas forcing.
Abstract. Secondary organic aerosol (SOA) is a dominant contributor of fine particulate matter in the atmosphere, but the complexity of SOA formation chemistry hinders the accurate representation of SOA in models. Volatility-based SOA parameterizations have been adopted in many recent chemistry modeling studies and have shown a reasonable performance compared to observations. However, assumptions made in these empirical parameterizations can lead to substantial errors when applied to future climatic conditions as they do not include the mechanistic understanding of processes but are rather fitted to laboratory studies of SOA formation. This is particularly the case for SOA derived from isoprene epoxydiols (IEPOX SOA), for which we have a higher level of understanding of the fundamental processes than is currently parameterized in most models. We predict future SOA concentrations using an explicit mechanism and compare the predictions with the empirical parameterization based on the volatility basis set (VBS) approach. We then use the Community Earth System Model 2 (CESM2.1.0) with detailed isoprene chemistry and reactive uptake processes for the middle and end of the 21st century under four Shared Socioeconomic Pathways (SSPs): SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. With the explicit chemical mechanism, we find that IEPOX SOA is predicted to increase on average under all future SSP scenarios but with some variability in the results depending on regions and the scenario chosen. Isoprene emissions are the main driver of IEPOX SOA changes in the future climate, but the IEPOX SOA yield from isoprene emissions also changes by up to 50 % depending on the SSP scenario, in particular due to different sulfur emissions. We conduct sensitivity simulations with and without CO2 inhibition of isoprene emissions that is highly uncertain, which results in factor of 2 differences in the predicted IEPOX SOA global burden, especially for the high-CO2 scenarios (SSP3–7.0 and SSP5–8.5). Aerosol pH also plays a critical role in the IEPOX SOA formation rate, requiring accurate calculation of aerosol pH in chemistry models. On the other hand, isoprene SOA calculated with the VBS scheme predicts a nearly constant SOA yield from isoprene emissions across all SSP scenarios; as a result, it mostly follows isoprene emissions regardless of region and scenario. This is because the VBS scheme does not consider heterogeneous chemistry; in other words, there is no dependency on aerosol properties. The discrepancy between the explicit mechanism and VBS parameterization in this study is likely to occur for other SOA components as well, which may also have dependencies that cannot be captured by VBS parameterizations. This study highlights the need for more explicit chemistry or for parameterizations that capture the dependence on key physicochemical drivers when predicting SOA concentrations for climate studies.
Climate considerations must become an element of procedural analysis. Scholars, rule makers, legislators, and judges have historically prioritized procedural values including cost, speed, and accuracy. More recent waves of scholarship—including critical perspectives—have emphasized participation as a further procedural goal. Yet this list has grown stale. Current environmental realities force all areas of law, including civil procedure, to reckon with climate change. In the future, proceduralists will be forced to integrate climate-aware thinking into their analyses, proposals, and discussions. Predictably, minds will disagree on how to accomplish this and the kinds of trade-offs that are warranted. Yet the fundamental point of this article remains: climate change resilience is a procedural concern that has not been recognized but must be. This signals not the genesis of a new branch of procedural scholarship but an opportunity to broaden the discussion for all procedural scholarship. In addition, this Article serves as a guide for rule makers and legislators who have just recently begun to show interest in climate-aware procedures. It directs them to procedural topics that require urgent attention, provides implementation strategies for developing resilient procedures, and considers the attendant costs and benefits of different approaches ranging from laissez-faire to aggressive engagement. Just as civil procedural innovations contributed to the emergence and success of the civil rights movement, our collective response to climate change will also be shaped by civil procedure. This Article prepares for this future and offers a corrective to the field’s failure to anticipate and respond to climate change.
Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.
Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.
Hurricanes are expected to intensify throughout the 21st century, yet the impact of frequent major hurricanes on tropical ecosystems remains unknown. To investigate tropical forest damage and recovery under different hurricane regimes, we generate a suite of scenarios based on Coupled Model Intercomparison Project Phase 6 climate projections and increased hurricane recurrence and intensity for the Luquillo Experimental Forest, Puerto Rico. We then use the Ecosystem Demography model to predict changes in carbon stocks, forest structure and composition. In this work, our results indicate that frequent hurricane disturbances in the future would decrease the overall aboveground biomass, decrease the dominance of late-successional species, but increase the dominance of palm species. Warmer climates with increased CO 2 would have little effect on the functional-type composition but increase the aboveground biomass. However, the predicted climate and CO 2 fertilization effects would not compensate for the biomass loss due to more frequent severe-hurricane disturbances.