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Item EXPLORING AND ASSESSING LAND-BASED CLIMATE SOLUTIONS USING EARTH OBSERVATIONS, EARTH SYSTEM MODELS, AND INTEGRATED ASSESSMENT MODELS(2024) Gao, Xueyuan; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Anthropogenic greenhouse gas (GHG) emissions have led the global mean temperature to increase by approximately 1.1 °C since the industrial revolution, resulting in mass ice sheet melt, sea level rise, and an increase in extreme climate events, and exposing natural and human systems to uncertainties and the risks of unsustainable development. Meeting the Paris Agreement’s climate goal of keeping temperature increases well below 2 °C — even 1.5 °C — will require removing CO2 from the atmosphere beyond reducing GHG emissions. Therefore, carbon dioxide removal and the sustainable management of global carbon cycles are one of the most urgent society needs and will become the major focus of climate action worldwide. However, research on carbon dioxide removal remains in an early stage with large knowledge gaps. The global potential and scalability, full climate consequences, and potential side effects of currently suggested carbon sequestration options — afforestation and reforestation, bioenergy with carbon capture and storage (BECCS), direct air carbon capture — are uncertain. Moreover, although about 120 national governments have a net-zero emission target, few have actionable plans for developing carbon dioxide removal.This dissertation examines two major categories of land-based carbon removal and sequestration methods: nature-based solutions that rely on the natural carbon uptake of the land ecosystem, and technology-based solutions, especially BECCS. These two options were investigated using four studies with satellite and in-situ observations, Earth system models (climate models), and integrated assessment models (policy models). Study 1 provides evidence that land ecosystem is an important carbon sink, Study 2 assesses the carbon sequestration potential of forest sustainable management via numerical experiments, Study 3 monitors recent tropical landscape restoration efforts, and Study 4 extends to BECCS and explores the impacts of future climate changes on its efficacy. Overall, this dissertation (1) improved monitoring, reporting, and verification of biomass-based carbon sequestration efforts using Earth observations, (2) improved projections on biomass-based carbon sequestration potential using Earth system models and socio-economic models, and (3) provided guidance on scaling up biomass-based carbon sequestration methods to address the climate crisis.Item Monitoring Aboveground Biomass in Forest Conservation and Restoration Areas Using GEDI and Optical Data Fusion(2024) Liang, Mengyu; Duncanson, Laura I; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Forests play a critical role in the global carbon cycle by sequestering carbon in the form of aboveground biomass. Area-based conservation measures, such as protected areas (PAs), are a cornerstone conservation strategy for preserving some of the world's most at-risk forest ecosystems. Beyond PAs, tree planting and forest restoration have been lauded as solutions to combat climate change and criticized as ways for polluters to offset carbon emissions. Consistent monitoring and quantification of forest restoration can impact decisions on future restoration activities. In this dissertation, I utilized a fusion of remote sensing assets and a combination of remote sensing with impact assessment techniques, to obtain objective baseline information for reconstructing past forest biomass conditions, and for monitoring and quantifying the patterns and success of forest regrowth in areas that underwent different forest management interventions. This overarching research goal is approached in three studies corresponding to chapters 2-4. In chapter 2, PAs’ effectiveness in storing biomass carbon and preserving forest structure is assessed on a regional scale using Global Ecosystem Dynamics Investigation (GEDI) lidar data in combination with a counterfactual analysis using statistical matching. This chapter provides an assessment of the reference condition of the biomass carbon storage capacity by one of the most stringent forest management means. The study finds that analyzed PAs in Tanzania possess 24.4% higher biomass densities than their unprotected counterparts and highlights that community-governed PAs are the most effective category of PAs at preserving forest structure and aboveground biomass density (AGBD). In chapter 3, empirical models are developed to link current (2019-2020) AGBD estimates from the GEDI with Landsat (2007-2019) at a regional scale. This will allow both current wall-to-wall biomass mapping and estimation of biomass dynamics across time. We demonstrate the utility of the method by applying it to quantify the AGBD dynamics associated with forest degradation for charcoal production. In chapter 4, the same modeling framework laid out in chapter 3 will be used to derive AGBD trajectories for 27 forest restoration sites across three biomes in East Africa. To assess the effectiveness of and compare Assisted Natural Regeneration (ANR) and Active Restoration (AR) in enhancing forest AGBD growth compared to natural regeneration (NR), we used staggered difference-in-difference (staggered DiD) to analyze the average annual AGBD change. We controlled for pre-intervention AGBD change rate between AR/ANR and NR and estimated the effectiveness with explicit consideration of intervention duration. This study finds that AR and ANR outperform NR during long-term restoration. Using the most suitable restoration interventions in each biome and timeframe, 4% suitable areas could enhance 2.40 ± 0.78 Gt (billion metric tons) forest carbon uptake over 30 years, equivalent to 3.6 years of African-wide emissions. Overall, this dissertation develops remote sensing methodological frameworks for using GEDI data and its fusion with Landsat time series to quantify and monitor forest AGBD. Moreover, by combining remote sensing-derived AGBD dynamics with impact assessment techniques, such as statistical matching and staggered DiD, the dissertation further assesses and compares different conservation and restoration means’ effectiveness in increasing AGBD and carbon uptake in forests. The dissertation therefore advances the applications of state-of-the-art remote sensing data and techniques for sustainably managing forests towards climate mitigation targets.Item Demand-Driven Climate Mitigation in the United States: Challenges and Opportunities to Reduce Carbon Footprints from Households and State-Level Actors(2022) Song, Kaihui; Baiocchi, Giovanni; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Subnational and non-governmental actors have great potential to push for bolder climate actions to limit the global average temperature increase to 1.5 degrees Celsius above pre-industrial levels. A consistent and accurate quantification of their GHG emissions is an important prerequisite for the success of such efforts. Although an increasing number of subnational actors have developed their climate mitigation plans with medium- or long- term goals, whether these progressive commitments can yield effectiveness as planned still remains unclear. This dissertation research focuses on two large groups of climate mitigation actors in the U.S. – households and state-level actors – to improve the understanding of potential mitigation challenges and shed light on climate policies. This dissertation consists of three principle essays. The first essay reveals a key challenge of emission spillover among state-level collective mitigation efforts in the U.S. It quantifies consumption-based GHG emissions at the state level and analyzes emissions embodied in interstate and international trade. By analyzing major emission transfers between states from critical sectors, this essay proposed potential policy strategies for effective climate mitigation collaboration. The second essay addresses unequal household consumption and associated carbon footprints in the U.S., with a closer look at different contributions across income groups to the national peak-and-decline trend in the U.S. This analysis further analyzes changes in consumption patterns of detailed consumed products by income groups. The third essay proposed a framework to link people’s needs and behaviors to their consumption and associated carbon footprints. This framework, built on existing models that connect carbon footprints with consumer behaviors, extends to people’s needs with simulation over time. Such an extension provides a better understanding of carbon footprints driven by various needs in the context of real-world decision-making. Based on this framework, this essay selects a basket of behavioral changes driven by changing fundamental human needs and analyzes associated carbon footprints. The dissertation identifies opportunities and challenges in demand-driven climate mitigation in the U.S. Its findings provide implications for effective climate actions from state-level actors and households.Item Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics(2021) Ma, Lei; Hurtt, George; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Quantifying, attributing, and projecting terrestrial carbon dynamics can provide valuable information in support of climate mitigation policy to limit global warming to 1.5 °C. Current modeling efforts still involve considerable uncertainties, due in part to knowledge gaps regarding efficient and accurate scaling of individual-scale ecological processes to large-scale dynamics and contemporary ecosystem conditions (e.g., successional states and carbon storage), which present strong spatial heterogeneity. To address these gaps, this research aims to leverage decadal advances in land-use modeling, remote sensing, and ecosystem modeling to improve the projection of terrestrial carbon dynamics at various temporal and spatial scales. Specifically, this research examines the role of land-use modeling and lidar observations in determining contemporary ecosystem conditions, especially in forest, using the latest land-use change dataset, developed as the standard forcing for CMIP6, and observations from both airborne lidar and two state-of-the-art NASA spaceborne lidarmissions, GEDI and ICESat-2. Both land-use change dataset and lidar observations are used to initialize a newly developed global version of the ecosystem demography (ED) model, an individual-based forest model with unique capabilities to characterize fine-scale processes and efficiently scale them to larger dynamics. Evaluations against multiple benchmarking datasets suggest that the incorporation of land-use modeling into the ED model can reproduce the observed spatial pattern of vegetation distribution, carbon dynamics, and forest structure as well as the temporal dynamics in carbon fluxes in response to climate change, increased CO2, and land-use change. Further, the incorporation of lidar observations into ED, largely enhances the model’s ability to characterize carbon dynamics at fine spatial resolutions (e.g., 90 m and 1 km). Combining global ED model, land-use modeling and lidar observation together can has great potential to improve projections of future terrestrial carbon dynamics in response to climate change and land-use change.Item PARTICIPATION IN CLIMATE CHANGE ADAPTATION: THE ROLE OF SOCIAL NETWORKS IN SUPPORTING LEARNING AND COLLECTIVE ACTION(2020) Teodoro Morales, Jose Daniel; Prell, Christina; Sun, Laixiang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Climate change is a complex problem affecting the world in different ways and posing challenges at varying governance levels. It is widely acknowledged that broad stakeholder participation is needed to adapt to increasing climate impacts. However, interactions between stakeholders are complex and not enough is known about the social processes that support stakeholder participation or how to measure its effectiveness. The main goal of this dissertation is to increase the understanding of stakeholder participation in addressing climate change problems. Using the State of Maryland (USA) as a case study, I (1) evaluate the magnitude of climate change impacts and map the stakeholder landscape in this region, and (2) I focus on a local participatory process in the eastern shore of the Chesapeake Bay, the Deal Island Peninsula Partnership (DIPP), to study how stakeholder networks facilitate learning and collective action. I found the Chesapeake Bay is experiencing severe impacts from sea-level rise, scientists and state government produce more data and indicators at larger scales, while fewer data are produced at the local level where is needed. Increasingly, participatory approaches are being employed to bridge the knowledge gap between experts, scientists, and local stakeholders. Moreover, I found that DIPP stakeholder views are predicted by their social networks of mutual understanding, respect, and influence. Finally, by modeling the co-evolution of mutual understanding ties, co-attendance, and climate change perceptions, I found that stakeholder participation enables stronger and denser social networks of mutual understanding, yet these ties do not facilitate changes in perceptions. These results suggest that fostering mutual understanding among a diverse group of stakeholders may be more relevant for collective action than changing their perceptions. This dissertation provides empirical evidence that stakeholder participation is important in climate adaptation policies and contributes to the development of measures for stakeholder participation effectiveness.Item Quantifying the Spatial and Temporal Variation of Land Surface Warming Using in situ and Satellite Data(2019) Rao, Yuhan; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The global mean surface air temperature (SAT) has demonstrated the “unequivocal warming”. To understand the impact of the global warming, it is very important to quantify the spatial and temporal patterns of the surface air temperature change. Currently, most observational studies rely on in situ temperature measurements over the land and ocean. But the uneven and sparse nature of these temperature measurements may cause large uncertainty for the climate analysis especially at local and regional scales. With the rapid development of satellite data, it is possible to estimate spatial complete surface air temperature from satellite data using advanced statistical models. The satellite data-based estimation can serve as a better data source for local and regional climate analysis to reduce analysis uncertainty. In this dissertation, I firstly examined the uncertainty of four mainstream gridded SAT datasets over the global land area (i.e., BEST-LAND, CRU-TEM4v, NASA-GISS, NOAA-NCEI). The comprehensive assessment of these datasets concludes that different data coverage may cause remarkable differences (i.e., -0.4 ~ 0.6°C) of calculated large scale (i.e., global, hemispheric) average SAT anomaly using different datasets. Moreover, these datasets show even larger differences at regional and local scale (5°×5°). The local and regional data differences can lead to statistically significant differences on linear trends of SAT estimated using different datasets. The correlation analysis shows strong relationship between the uncertainty of estimated SAT trends and the density of in situ measurements across different regions. To reduce the uncertainty of surface air temperature data, I developed a statistical modelling framework which can estimate daily surface air temperature using remote sensing land surface temperature and radiation products. The framework uses machine learning models (i.e., rule-based Cubist regression model and multivariate adaptive regression spline) to characterize the physical difference between land surface temperature and surface air temperature by including radiation products at both surface and the top of the atmosphere. The model was firstly developed for the Tibetan Plateau using Cubist model trained with Chinese Meteorological Administration station measurements. Comprehensive evaluation show that the Cubist model can estimate the surface air temperature with nearly zero degree Celsius bias and small RMSEs between 1.6 °C ~ 2.1 °C. The estimated SAT over the entire Plateau for 2000-2015 show that the warming of the western part of the Plateau has been more prominent than the rest of the region. This result show the potential underestimation of conventional station measurements based studies because there are no station measurements to represent the rapid warming region. The machine learning model is then extended to the northern high latitudes with necessary modification to account for the regional difference of the diurnal temperature cycle as well as the large data volume of the northern high latitudes. The MARS model trained using data over the northern high latitudes from the Global Historical Climatology Network daily data archive show a reasonable model performance with the bias of around -0.2 °C and the RMSE ranging between 2.1 – 2.6 °C. Further evaluation shows that the model performs worse over permanent snow and ice surface due to the insufficient training data to represent this specific surface conditions. Overall, this research demonstrated that leveraging advanced statistical methods and satellite products can help generating high quality surface air temperature data which can provide much needed spatial details to reduce the uncertainty of local and regional climate analysis. The model developed in this research is generic and can be further extended to other regions with proper modification and training using high quality local data.Item Outgoing Longwave Radiation at the Top of Atmosphere: Algorithm Development, Comprehensive Evaluation, and Case Studies(2019) Zhou, Yuan; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Outgoing longwave radiation (OLR) at the top of the atmosphere (TOA) represents the total outgoing radiative flux emitted from the Earth’s surface and atmosphere in the thermal-infrared wavelength range. It plays a role as a powerful diagnostic of Earth’s climate system response to absorbed incoming solar radiation (ASR). Long-term measurements of OLR are essential for quantitatively understanding the climate system and its variability. However, inconsistencies and uncertainties have been always existing in OLR estimation among different datasets and algorithms. The objective of this dissertation is to carry out a comprehensive investigation on OLR with three specific questions: 1) How large are the discrepancies in estimates from various OLR products and what are their spatial and temporal patterns? 2) How to generate more accurate and more useful OLR estimates from multi-spectral satellite observations? 3) How does OLR respond to extreme climate and geological events such as El Niño/Southern Oscillation (ENSO) and giant earthquakes, and does the newly developed OLR products have any advantage to predict such events? To address those questions, this dissertation 1) conducts comprehensive evaluations on multiple OLR datasets by performing inter-comparisons among different satellite retrieved OLR products and different reanalysis OLR datasets, respectively; 2) develops an algorithm framework for estimating OLR from multi-spectral satellite observations based on radiative transfer simulations and statistical approaches; 3) investigates the correlation between OLR anomalies and historical ENSO events and a typical giant earthquake, and makes an attempt to predict ENSO and earthquake through OLR variations. Results indicate that 1) obvious discrepancies exist among different OLR datasets, with the two Japanese Meteorological Agency’s (JMA) Japanese Reanalysis project (JRA) OLRs displays the largest differences with others. However, all OLR products and datasets have comparable magnitude of inter-annual variability and monthly/seasonally anomaly, resulting in similar capability to capture the tropical expansion and ENSO events; 2) the developed OLR algorithm framework can generate reliable OLR estimates from multi-spectral remotely sensed data including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR); 3) OLR has a potential to predict ENSO events through traditional statistical approach and machine learning methods, and it has slight advantage over the sea-surface-temperature (SST) as a metric for this purpose. The developed high resolution AVHRR OLR performs better than High-Resolution Infrared Radiation Sounder (HIRS) and NOAA interpolated AVHRR OLR in predicting ENSO. In addition, the singularities in OLR spatial anomalies around the giant earthquake epicenter starting three days prior to the earthquake days also suggests the OLR as an effective precursor of such an event, and the developed AVHRR OLR showed much stronger sensitivity to the coming earthquake than the existing NOAA interpolated AVHRR OLR, suggesting that the former one as a better indicator for the earthquake prediction. In this dissertation, the in-depth inter-comparisons among various OLR datasets will contribute as a reference for peers in the climate community who use OLR as one of inputs in their climate models or other diagnostic purpose. The developed OLR algorithm framework could be utilized to estimate OLR from future multi-spectral satellite data. This study also demonstrates that OLR is a promising indicator to predict ENSO and testifies that it is a precursor of giant earthquakes, which has implications for decision making aimed at alleviating the impacts on life and property from these extreme climate variations through some preventive measures such as releasing weather alert and conducting evacuations.Item Forest Cover Dynamics of Shifting Cultivation in the Democratic Republic of Congo(2018) Molinario, Giuseppe Maria; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation is focused on contextualizing spatio-temporally forest cover loss in the DRC for the period 2000-2015 as it relates to the shifting cultivation dynamic and the rural complex mosaic. Impacts of forest loss on forest ecosystems, carbon release and biodiversity habitat differ depending on where and when it occurs relative to the rural complex. This was done by mapping the rural complex and disaggregating forest cover loss due to cyclical, livelihood shifting cultivation within three areas: 1) the baseline established rural complex (ERC) for 2000 and new 2000-2015 primary forest loss occurring as either 2) rural complex expansion (RCE) or 3) isolated forest perforations (IFP) further into core forest. Finally the influence of large-scale commercial land uses on forest cover loss is also assessed, from a spatial perspective. Between 2000 and 2010 the rural complex grew by 10% from 12% to 13% of the DRC’s land area, at an average yearly rate of 1%, while perforated forest grew by 74%, from 0.8% to 1.5% of DRC’s land area in 2010 at an average yearly rate of 0.7%. Core forest decreased by -3.8% at an average yearly rate of -0.4% per year, from 38% to 36.6% of the 2010 land area. Of particular concern is the nearly doubling of perforated forest, representing greater spatial intrusion of forest clearing within core forest areas. The land cover and land use (LCLU) components of the ERC were estimated by photo-interpreting high resolution imagery selected using a simple random sampling scheme. In the ERC 76% of land was already actively used for shifting cultivation. Therefore, together with remnant patches of primary forest (11%), an estimated 87% of the ERC was available for future shifting cultivation. Assuming a 4.6% clearing rate, this allowed estimating a ~18 year reuse rate of land in the ERC. Only 2% of the ERC area was occupied by large-scale commercial land use. This led to positing that commercial land uses might be more prevalent further away from settlements into core forest, where lower population density leads to less competition for natural resources. This hypothesis was tested by extending the probabilistic sampling analysis to new primary forest cover loss occurring outside of the ERC during the period 2000-2015. The map of the rural complex developed in Chapter 2 was validated, confirming larger proportions of primary forest and smaller proportions of shifting cultivation further away from the ERC and into core forest areas. LCLU proportions were established for both the RCE and IFP areas. Finally a concentric buffer distance analysis around sample points was used to quantify large-scale commercial land uses at the landscape scale, such as logging, mining and plantations that might be influencing shifting cultivation-driven forest cover loss. In the RCE the proportion of commercial land use was 0.4%, whereas it was 0.5% in IFPs; less than the proportion of commercial land use found in the ERC (2%). At the same time, results of the concentric buffer distance analysis show that 12% of sample points in the RCE and 9% of sample points in the IFP had commercial land uses within 5km. Commercial land uses are possibly more prevalent closer to the ERC because while there is more competition for land, there are also roads and communities that allow for the transportation of goods and provide labor. These results support the conclusion that large scale LCLU change dynamics in the DRC, such as commercial operations for export, are currently dwarfed by the reliance of rural populations on shifting cultivation. The vast majority of forest cover loss in the DRC remains due to smallholder farming not associated with commercial land uses. However, large-scale agroindustry or resource extraction activities lead to increased forest loss as their worker populations and communities rely on shifting cultivation for food, materials and energy. The spatial analysis of the rural complex allows us to peer into the future of forests in the DRC, as where isolated perforations lead, the rural complex soon follows and as the rural complex expands, so do commercial land uses.Item HIGH-VOLUME RAINFALL IMPACTS AND ADAPTATION IN THE U.S. MID-ATLANTIC UNDER CLIMATE CHANGE AND URBANIZATION(2018) Khan, Ibraheem Muhammad Pasha; Hubacek, Klaus; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Water over-abundance has negative effects on proper functioning of ecosystem services. The increase in heavy precipitation events and hence stormwater quantity, due to climate change and urbanization, is a major flooding concern. These events also affect ecosystem processes leading to soil erosion and sedimentation. This dissertation draws from different disciplines and involves quantification of hydrological extremes, assessment of stormwater management resilience and analysis of impacts on ecosystem services under anticipated future changes in the U.S. Mid-Atlantic region. In Chapter 2 of the dissertation, use of precipitation capture depth and findings of likely increase in heavy precipitation events is relevant to flooding concerns at small watershed scales (~3 km2) and are valuable planning-level information for municipal stormwater management. Estimates developed in this dissertation of changes in water volume and resultant on-site infrastructure costs can help stakeholders and managers in planning for flood mitigation and protection of ecosystem services. In addition, the use of capture depth percentiles such as d85, d90, d95, and d99, have the potential to serve as meaningful hydrologic indicators for stormwater management planning. In Chapter 3, the finding of likely higher erosion rates and sediment yield in the future is a point of concern and relevant for effective land use planning. The approach to estimate representative calibration values for sediment delivery ratio model, at small scale (~3 km2) urban watersheds, is valuable for ungauged sites replacing average or theoretical calibration values. In Chapter 4, the construction of a simple curve number watershed model with reasonably good performance and few input data needs offers a possible flow simulation tool for medium to highly impervious watersheds at small scales. Moreover, the stormwater management pathways along with cost-benefit assessment using green stormwater management practices serve as a first step to determine effectiveness of certain green practices at the watershed scale. It provides insights and help identify future research needs to fill gaps in our understanding of green stormwater management practices and how they affect ecosystem services.Item QUANTIFYING FIRE-INDUCED SURFACE FORCING IN SIBERIAN LARCH FORESTS(2017) Chen, Dong; Loboda, Tatiana V.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Wildfires are a common disturbance agent in the global boreal forests. In the North American boreal forests, they have been shown to exert a strong cooling effect through post-fire changes in surface albedo that has a larger overall impact on the climate system than associated carbon emissions. However, these findings are not directly applicable to the Siberian larch forests, a major component of the boreal biome where species composition are dominated by a deciduous needleleaf species and fire regimes are characterized by the common occurrence of both stand-replacing and less-severe surface fires. This dissertation quantifies the post-fire surface forcing imposed by both fire types in these forests over 14 years since fire, and determines that both surface and stand replacing fires impose cooling effects through increased albedo during snow season. The magnitude of the cooling effect from stand replacing fires is much larger than that of surface fires, and this is likely a consequence of higher levels of canopy damage after stand-replacing fires. At its peak (~ year 11 after fire occurrence), the cooling magnitude is similar to that of the North American boreal fires. Strong cooling effect and the wide-spread occurrence of stand-replacing fires lead to a net negative surface forcing over the entire region between 2002 and 2013. Based on the extended albedo trajectory which was made possible by developing a 24-year stand age map, it was shown that the cooling effect of stand-replacing fires lasts for more than 26 years. The overall cooling effect of surface fires is of lower magnitude and is likely indicative of damages not only to the canopies but also the shrubs in the understory. Based on the identified difference in their influences on post-fire energy budget, this dissertation also identified a remote sensing method to separate surface fires from stand-replacing fires in Siberian larch forests with an 88% accuracy. The insights gained from this dissertation will allow for accurate representation of wildfires in the regional or global climate models in the future.