Future Temperature Changes Research Articles

Climatic and anthropogenic impacts on the water balance of Issyk-Kul Lake through its main catchments

Lake Issyk-Kul is an endorheic lake in arid Central Asia that is vital to the region's ecological sustainability and socio-economic development. Climate change and anthropogenic water consumption led to fluctuations in the lake's water level, which affected the water resource. The goal of this study was to examine the impacts of climate change and human activities on the Issyk-Kul water balance by combining the Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios with hydrological modeling. The Soil and Water Assessment Tool (SWAT) model was used to incorporate signals of future precipitation and temperature changes. According to the scenarios, the total discharge of the three catchments showed an overall increasing trend with a maximum value of 28.02%. The snow and ice-melt water from March to August was revealed, and the increasing trends only occurred from March to May, with the snow and ice melting peak variations ranging from 0.5% to 2%. The high increase in change appeared in northern catchment of the lake. There was an exceptional upward precipitation trend over the northern catchment, with annual increases ranging from 0.7 to 14.5%, and an average annual temperature of 1.72°C. With slight similarities, the total runoff would increase for all catchments, with an average annual value of 10.6%. The northern catchment was significantly more sensitive to precipitation and warming than the southeastern catchments. Under land use land cover change, average annual discharge decreased with agricultural expansion, with discharge differences ranging from −0.005 to −1.06 m3/s. The findings are useful for decision-makers addressing the challenges of climate change mitigation and local water resource management.

The joint effects of mixture exposure to multiple meteorological factors on step count: A panel study in China

The public health burden of increasing extreme weather events has been well documented. However, the influence of meteorological factors on physical activity remains limited. Existing mixture effect methods cannot handle cumulative lag effects. Therefore, we developed quantile g-computation Distributed lag non-linear model (QG-DLNM) by embedding a DLNM into quantile g-computation to allow for the concurrent consideration of both cumulated lag effects and mixture effects. We gathered repeated measurement data from Henan Province in China to investigate both the individual impact of meteorological factor on step counts using a DLNM, and the joint effect using the QG-DLNM. We projected future step counts linked to changes in temperature and relative humidity driven by climate change under three scenarios from the sixth phase of the Coupled Model Intercomparison Project. Our findings indicate there are inversed U-shaped associations for temperature, wind speed, and mixture exposure with step counts, peaking at 11.6 °C in temperature, 2.7 m/s in wind speed, and 30th percentile in mixture exposure. However, there are negative associations between relative humidity and rainfall with step counts. Additionally, relative humidity possesses the highest weights in the joint effect (49% contribution). Compared to 2022s, future step counts are projected to decrease due to temperature changes, while increase due to relative humidity changes. However, when considering both future temperature and humidity changes driven by climate change, the projections indicate a decrease in step counts. Our findings may suggest Chinese physical activity will be negatively influenced by global warming.

Incorporation of climate change into a multiple stressor risk assessment for the Chinook salmon (Oncorhynchus tshawytscha) population in the Yakima River, Washington, USA.

One outcome of the 2022 Society of Environmental Toxicology and Chemistry Pellston Workshop on incorporating climate change predictions into ecological risk assessments was the key question of how to integrate ecological risk assessments that focus on contaminants with the environmental alterations from climate projections. This article summarizes the results of integrating selected direct and indirect effects of climate change into an existing Bayesian network previously used for ecological risk assessment. The existing Bayesian Network Relative Risk Model integrated the effects of two organophosphate pesticides (malathion and diazinon), water temperature, and dissolved oxygen levels on the Chinook salmon population in the Yakima River Basin (YRB), Washington, USA. The endpoint was defined as the entity, Yakima River metapopulation, and the attribute was defined as no decline to a subpopulation or the overall metapopulation. In this manner, we addressed the management objective of no net loss of Chinook salmon, an iconic and protected species. Climate change-induced changes in water quality parameters (temperature and dissolved oxygen levels) used models based on projected climatic conditions in the 2050s and 2080s by the use of a probabilistic model. Pesticide concentrations in the original model were modified assuming different scenarios of pest control strategies in the future, because climate change may alter pest numbers and species. Our results predict that future direct and indirect changes to the YRB will result in a greater probability that the salmon population will continue to fail to meet the management objective of no net loss. As indicated by the sensitivity analysis, the key driver in salmon population risk was found to be current and future changes in temperature and dissolved oxygen, with pesticide concentrations being not as important. Integr Environ Assess Manag 2024;20:419-432. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Future impacts of climate change on black spruce growth and mortality: review and challenges

Black spruce ( Picea mariana (Mill.) B.S.P.) is the dominant conifer species across a large part of North American boreal forests, providing many goods and services essential to human activities, and playing a major climatic role through the global carbon cycle. However, a comprehensive synthesis of the effects of climate change on black spruce has not yet been undertaken. The dynamics of black spruce are influenced by various living (biotic) and non-living (abiotic) factors, as well as their combined effects, which are particularly responsive to changes in climate. Climate change predictions suggest that northern ecosystems will experience the world's most significant impact. Therefore, black spruce is likely to undergo profound disruptions in its growth and mortality rate in the next few decades, resulting in significant changes in forestry and carbon storage. However, these changes will not be uniform throughout the entire distribution of the species. Future changes in temperature and precipitation will create more stress for water availability in the boreal forests of western and central North America than in their eastern counterparts. Thus, significant longitudinal and latitudinal gradients in tree growth and mortality variability are expected throughout the range of the species. This literature review aims to summarise the impacts of climate change on individual tree growth and mortality of this major species. While enhanced black spruce productivity could occur through both increased air temperature and nitrogen mineralisation in the soil, moisture limitation in central and western North America will result in significant growth reduction and mortality events across these regions. Conversely, under the expected climate change scenarios, black spruce forests may be more resilient in eastern North America, where climatic conditions appear more suitable, particularly in their northernmost range. In this review, we identify current research gaps for some disturbances, which should be addressed to better understand the impact of climate change on black spruce. Finally, we identify issues associated with sustainable forest management and the maintenance of black spruce under projected future climate changes.

Impacts of natural and anthropogenic forcings on historical and future changes in global-land surface air temperature in CMIP6–DAMIP simulations

Impacts of natural and anthropogenic forcings on historical and future changes in global-land surface air temperature in CMIP6–DAMIP simulations

The effect of water temperature changes on biological water quality assessment

Increasing temperatures caused by anthropogenic climate change are leading to changes in the composition of local communities across biomes. This has implications for ecological assessment methods that rely on macroinvertebrates as bioindicators of water quality. To investigate the influence of changing water temperature on these assessment methods, we analysed macroinvertebrate data from Swiss national monitoring programs. We used a species distribution model to simulate temperature change effects on macroinvertebrate communities and estimated the resulting changes on three biological indices commonly used in Switzerland, namely the species richness of Ephemeroptera, Plecoptera and Trichoptera (EPT), the Swiss biological (IBCH) index along with its components, as well as the species at risk pesticides (SPEARpesticides) index. While results vary by temperature scenario and index, our model results for the most realistic water temperature increase scenario of + 2 °C across most sites in Switzerland suggest no, or only a minor, influence of temperature (not accounting for other hydrological changes). Our model projection predicted only a small increase in the probability of occurrence for 70 % of the studied families. The sensitivity to temperature as captured in our model is generally not very high and varies among the biological indices: on average across all sites, a + 2 °C increase in temperature resulted in a 7 % increase in EPT species richness, a 4 % increase in the IBCH index, and a less than 1 % increase in the SPEARpesticides index. Our study suggests the robustness of these biological indices to moderate warming and points towards the usefulness of these biological indices for the next few decades as tools for water quality assessment. Despite some limitations of statistical species distribution models (e.g., not accounting for dispersal limitation or biotic interactions, predictive performance varying by taxon), the study provides valuable insights into the complex relationships between environmental factors and macroinvertebrate communities, and the potential impacts of future temperature change. These findings can inform conservation and management efforts for these important ecological systems.

Comparative global temperature prediction based on arima and bp neural networks

This paper focuses on the issue of global warming. Due to a large amount of alarming temperature reporting data in the past, with some countries hitting new temperature highs again and many countries declaring emergencies, this has raised concerns about future temperatures. Therefore, this paper predicts the future temperature changes based on historical data, and observes the future temperature trends in 2050 and 2100 as an example, focusing on comparing which one is more accurate in global temperature prediction, the ARIMA model or the BP neural network model. The data was first processed and then an ARIMA model was built using MATLAB and a BP neural network model was built using SPSS. These two models were used to describe the past and predict the future global temperature levels, and line graphs were plotted using SPSS. The two models predict that the temperature in 2050 will be 10.16 °C and 11 °C respectively, the temperature in 2100 will be 10.87 °C and 13 °C differently, and the time for the temperature to reach 20 °C will be 2202 and 2350 separately. Finally, the model was tested by regression analysis of the two models, and the goodness of fit of Arima model and BP neural network was calculated to be 0.726 and 0.84668 respectively, BP neural model is better.

Response of dissolved carbon dioxide and methane concentration to warming in shallow lakes

Shallow lake ecosystems are highly sensitive to temperature fluctuation because of their high water surface-to-volume ratios. Shallow lakes have been increasingly identified as a hotspot of CO2 and CH4 emissions, but their response to temperature variation remains unclear. Here, we report from a 5-month outdoor mesocosm experiment where we investigated the impacts of a projected 3.5 °C future warming and monthly temperature changes on lake CO2 and CH4, as well as the key drivers affecting the lake carbon cycling. Our results show that CO2 and CH4 concentrations had a significantly positive correlation with monthly temperatures. CH4 concentration was primarily regulated by monthly temperature, while nutrients effects on CO2 concentration overrode climate warming and temporal temperature changes. These findings imply the varied roles that temperature and nutrient levels can play on CO2 and CH4 dynamics in shallow lake systems. The relationship between temperature and CO2 concentration was nonlinear, showing a threshold of approximately 9 °C, at which CO2 concentration could be strongly modified by nutrient level in the lake systems. Understanding this complex relationship between temperature with CO2 and CH4 concentrations in shallow lakes is crucial for effective lake management and efficient control of greenhouse gases (GHGs) emissions.

Less negative impacts of climate change on crop yields in West Africa in the new CMIP6 climate simulations ensemble

Food insecurity is among one of the greatest risks posed by climate change in Africa, where 90 to 95% of African food production is rainfed and a large proportion of the population already faces chronic hunger and malnutrition. Although, several studies have found robust evidence of future crop yield losses under climate change scenarios, there is wide variation among crops and regions as well as large modeling uncertainties. A large part of this uncertainty stems from climate projections, as climate models may differ in simulating future changes in precipitation and temperature, which could lead to different future crop production scenarios. This work examines the impacts of climate change on crop yields of maize, millet and sorghum in West Africa using climate change projections from the Coupled Model Intercomparison Project 5th Phase (CMIP5) and from the new generation of climate models from the Coupled Model Intercomparison Project 6th Phase (CMIP6). We use the SIMPLACE crop modeling framework to simulate historical and future crop yields, and bootstrap techniques to evaluate projected changes in crop productivity between the CMIP5 and CMIP6 ensembles. Using the new generation of climate models CMIP6, we find that the negative crop yield projections shown by CMIP5 simulations are largely reduced, with even large increases in crop yields when the effect of atmospheric CO2 concentration is considered in the crop model. These differences in crop yield impacts between the CMIP5 and CMIP6 simulations are mainly due to different climate projections of temperature and precipitation in West Africa; CMIP6 projections being significantly wetter and cooler by mid-century and to a lesser extent by the end of the century. Such results highlight the large uncertainties that remain in assessing the impacts of climate change in the region and the consequent difficulty for end-users to anticipate adaptation strategies.

Proximate cues of flowering in a subtropical rain forest

AbstractPlants have evolved mechanisms to track seasonal variation in environmental resources, enabling them to time key life‐history events to appropriate seasons. While the proximate cues for flowering initiation are well documented in the temperate region, it is still unclear what the flowering cues are in the tropics, especially in the subtropics. Our study compared first flowering dates (FFDs) predicted by eight hypothesized proximate cues concerning photoperiod, mean and directional changes in solar irradiance and warm/cool temperature, and rainfall with flowering dates observed over 19 years of weekly monitoring for 16 species in a subtropical rain forest. We observed considerable interannual variation in the median FFDs for the study species, ranging from 21 to 101 days. The early‐spring flowering species tended to have greater interannual variation in FFDs than the summer flowering species. For 13 study species, temperature cues best explained interannual variation in FFDs. Cool temperatures in the previous fall/winter and warm temperatures in the current spring (or previous summer) might trigger the onset of flowering in these 13 species. Cues associated with photoperiod and irradiance also predicted interannual variation in FFDs with small root mean square error (<1.5 census intervals) for 12 species but generally had higher prediction errors than temperature‐related cues. Cues associated with seasonal variation in rainfall failed to predict flowering times in any species. Our results suggest that future changes in temperature may alter flowering times for most species in subtropical forests, leading to changes in ecosystem processes and biosphere feedback to the climate system.Abstract in Chinese is available with online material.

Evaluation of future climatology and its uncertainty under SSP scenarios based on a bias processing procedure: A case study of the Lancang-Mekong River Basin

The Lancang-Mekong River is the most important international river in Southeast Asia, and its water resources are invaluable for the survival of the inhabitants of the basin, while the climatic conditions have a significant impact on the production and livelihoods of the riparian countries. In this study, future climate change in the Lancang-Mekong River Basin (LMRB) was projected based on precipitation and temperature from CMIP6. First, a bias processing procedure was established to correct the initial bias of climate models using the Daily Bias Correction (DBC) method, and the performance of equal weighted averaging (EW) and Bayesian mode averaging (BMA) of multi-model ensembles was compared. The characteristics of future precipitation and temperature changes were projected in terms of mean climatology, seasonal variation and extreme events, respectively, from which the future trends of the four weather extremes (dry-cold, dry-hot, wet-cold and wet-hot) were projected. Evaluation based on mean climatology, daily frequency distribution, temporal change and its bias showed that the climate bias correction procedure has good performance. It was found that future precipitation in the LMRB will increase by 50–120 mm in the near future (2031–2050) and 150–400 mm in the far future (2061–2090), with higher increases in the upper Mekong River Basin (MRB), Lower MRB and Mekong Delta than in the Lancang River Basin (LRB) and middle MRB. Future increase in precipitation will occur mainly from June to October, while April and May will see a decrease by 10–30 mm, and the number of heavy precipitation days (R25, daily precipitation exceeding 25 mm) will increase by about 5 days in the eastern part of the lower and middle MRB. The mean temperature will increase by 1–3 °C in most parts of the LMRB, with the LRB experiencing an increase of about 3 °C higher than in the MRB. Future maximum and minimum daily temperatures will increase by 1–2 °C in the near future and by 2–5 °C in the far future. Dry-hot days will increase by about 20 days in the lower and middle MRB, and wet-hot days will increase significantly in the MRB, by 30–80 days in the near future and by 50–100 days in the far future.

Future changes of summer monsoon rainfall and temperature over Bangladesh using 27 CMIP6 models

This research aims to investigate the future changes in summer monsoon rainfall and temperature in Bangladesh. The study revealed that INM-CM5-0 is the best model for projecting temperature, while BCC-CSM2-MR is the best model for projecting rainfall over Bangladesh. Using data from a large ensemble of 27 models from CMIP6, the study examined the rainfall and temperature change projections of Bangladesh during the twenty first century relative to the reference period (1981–2014) under SSP2–4.5 and SSP5–8.5. Under SSP2-4.5 and SSP5-8.5, the multi-model ensemble monsoon mean rainfall over Bangladesh will fluctuate between 40 and 260 mm and 100 and 900 mm, respectively. In most parts of the country’s north, northeastern, and western regions, the projected changes in spatial patterns of monsoon rainfall indicate an increase in rainfall. The projected temperature indicated that Bangladesh’s northwest and west-central areas could face the most significant rise in temperatures, surpassing 3.8 °C under SSP5-8.5.

Projection and Uncertainty Analysis of Future Temperature Change over the Yarlung Tsangpo-Brahmaputra River Basin Based on CMIP6

Accurate predictions of future climate change are significant to both human social production and development. Accordingly, the changes in the daily maximum (Tmax) and minimum temperatures (Tmin) in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB), along with its three sub-regions (Tibetan Plateau—TP, Himalayan Belt—HB, and Floodplain—FP) were evaluated here using the Bayesian model average (BMA) results from nine climate models in the CMIP6 under four future scenarios, and the corresponding uncertainty of the projected results was analyzed. The results showed the following: (1) The BMA can simulate the Tmax and Tmin of the YBRB well. (2) Future Tmax and Tmin over the YBRB exhibited an overall fluctuating upward trend. Even under the most ideal sustainable development scenario examined (SSP126), the average Tmax (Tmin) over the YBRB was projected to increase by 3.53 (3.38) °C by the end of this century. (3) Although the future changes in the YBRB are predicted to fall below the global average, the future temperature difference in the YBRB will increase further. (4) The uncertainty increased with prediction time, while spatially, the regions with the uncertainty were the TP > HB > FP. These findings can provide a reference for the YBRB climate change adaptation strategies.

Multi-Level Modelling of Global Warming Based on Machine Learning Algorithms

Today, abnormally high temperatures around the world are a wake-up call for global warming. Global warming is a problem that needs to be addressed urgently. In order to assess past temperature changes, this paper constructs indicators of data growth rates over a certain period of time. Firstly, this paper analyses the changes in growth rates after 1800. In addition, this study uses the Mann-Kendall method to determine the trend of the growth rate, and the ridge regression model, ARIMA model, and LSTM model are constructed to predict the future temperature change, respectively. The results show that the LSTM model is the most reliable, whereas the ARIMA model is only suitable for short-term forecasts due to the lack of a follow-up dataset and is less effective in predicting global temperatures below 20°C. The results of the ARIMA model are summarised as follows, with polynomial fitting results being affected by the extremes of the data, and performing the worst. The results show that the temperature will reach 20°C around 2310 and within 2398~2439 according to the ridge regression model and LSTM model, respectively.

Climate change over South America simulated by the Brazilian Earth system model under RCP4.5 and RCP8.5 scenarios

Climate projections simulated by the Brazilian Earth System Model (BESM2.5) under RCP4.5 and RCP8.5 climate scenarios are analyzed based on future changes of surface air temperature (SAT) and precipitation with respect to the historical reference period 1971–2000. Since BESM2.5 is the only climate model developed in a South American country, this study gives a particular emphasis to South American future climate projections. Regarding the surface air temperature, BESM2.5 projects a steady warming throughout the 21st century, with the highest warming over eastern Amazonia, northern Chile and central South America for both scenarios. The SAT changes range between 2 °C and 3–4 °C for RCP4.5 and RCP8.5, respectively. On the other hand, projected precipitation varies over different regions of South America, with decreasing and increasing trends over the Amazon and southern South America, respectively. Interestingly, this study shows contrasting results with respect to extreme precipitation indicators, projecting enhanced extreme events with higher numbers of both consecutive dry days and days in which the precipitation exceeds 20 mm over the southeastern region. The model projects a meridional dipole pattern in the precipitation, with decreasing precipitation and longer dry spells over Northeast Brazil and the East Amazon region and increasing precipitation and shorter dry spells over Northwest South America and West Amazon, that is driven by future changes in the SLP that imposes a meridional gradient over these regions, causing the increase of westerlies that are likely to increase the moisture transport from the Pacific Ocean into western South America and the weakening of the easterlies that transport moisture over eastern South America and East Amazon.

Regional Climate Simulation Ensembles within CORDEX-EA Framework over the Loess Plateau: Evaluation and Future Projections

As a semi-arid to semi-humid transitional zone, the Loess Plateau is sensitive to climate change due to its fragile ecological environment and geographic features. This study assesses the performance of six historical experiments from the Coordinated Regional Climate Downscaling Experiment (CORDEX) in this region during 1980–2005. In addition, projected future changes in surface air temperature and precipitation are investigated under the representative concentration pathways (RCP) 2.6 and 8.5 during three periods in the 21st century: the early future (2011–2040), middle future (2041–2070), and late future (2071–2099). Results show that experiments reasonably reproduce the spatial pattern of 2m temperature and precipitation for all seasons, yet with a slight warm bias and prominent wet bias. In the future, the area-averaged magnitude of change will be 1.1 °C, 1.4 °C, and 1.4 °C under RCP2.6 and 1.3 °C, 2.7 °C, and 4.5 °C under RCP8.5 for the early, middle, and late periods, respectively. The warming effect is greater in elevated areas. Precipitation change in future periods is more complex, with both increasing and decreasing trends, depending on the season, location, and scenario. The results are expected to provide regional climate information for decision makers and benefit applications such as agriculture, ecological environment protection, and water resource management.

Modelling potential distribution of Tuta absoluta in China under climate change using CLIMEX and MaxEnt

AbstractThe Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) originated in South America and is a major pest of the economically critical solanaceous crops. It is devastating to tomatoes, attacking mainly the leaves, stems, flowers and fruits of tomato plants, and can cause up to 100% damage. T. absoluta invaded China in 2017 and expanded rapidly, severely impacting the tomato industry. To illustrate the detailed potential distribution of T. absoluta in China, we used CLIMEX and MaxEnt models to predict the potential distribution of this pest using historical and future climate data. CLIMEX predicts a wider potential distribution area for T. absoluta in China than MaxEnt, which suggests that most of China is suitable for its distribution, except for the Tarim Basin in southern Xinjiang, western Inner Mongolia and northwestern Gansu. Both models accurately predicted the known distribution of T. absoluta in the provinces of Yunnan and Guangxi, and the predictions by both models suggest the total distribution range of T. absoluta in China is to increase slightly with future changes in climate temperature. These predictions will help understand the influence of climate change on the potential distribution of T. absoluta in China and thus provide a theoretical foundation for developing early monitoring, quarantine and control strategies.

Evaluation and Projection of Climate Change in the Second Songhua River Basin Using CMIP6 Model Simulations

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

Assessing future precipitation and temperature changes for the Kesinga Basin, India according to CORDEX-WAS climate projections

Assessing future precipitation and temperature changes for the Kesinga Basin, India according to CORDEX-WAS climate projections

Future Implications of Climate Change on Arum palaestinum Boiss: Drought Tolerance, Growth and Production

The objectives of the work were to understand the potential future climate changes in the Mediterranean region, assess the drought tolerance of the black calla lily (Arum palaestinum Boiss.), and investigate the mechanisms associated with its ability to withstand drought conditions. The Shared Socioeconomic Pathways (SSPs) of the Intergovernmental Panel on Climate Change (IPCC) were used to predict future temperature and precipitation changes. Both the SSP2-4.5 and SSP5-8.5 scenarios predicted a general increase in minimum and maximum temperatures and a decrease in precipitation. The projected increase in minimum temperature ranged from 2.95 °C under SSP2-4.5 to 5.67 °C under SSP5-8.5. The projected increase in maximum temperature ranged from 0.69 °C under SSP2-4.5 to 3.34 °C under SSP5-8.5. The projected decrease in precipitation ranged from −1.04 mm/day under SSP2-4.5 to −1.11 mm/day under SSP5-8.5. Results indicated that drought significantly impacted the physiological responses of the black calla lily. As drought increased, the black calla lily showed a reduction in leaf characteristics and non-structural carbohydrates, while proline content and reducing sugar content were increased, enhancing drought tolerance through osmoregulation. The black calla lily tolerates drought at a total ET of up to 50%. It has the potential to adapt to expected climate change through osmoregulation or by building a carbon and nitrogen sink for stress recovery.