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Abstract
Achievement of the United Nations Sustainable Development Goals (SDGs) is contingent on understanding the potential interactions among human and natural systems. In Kenya, the goal of conserving and expanding forest cover to achieve SDG 15 “Life on Land” may be related to other SDGs because it plays a role in regulating some aspects of Kenyan precipitation. We present a 40-yr analysis of the sources of precipitation in Kenya and the fate of the evaporation that arises from within Kenya. Using MERRA-2 climate reanalysis and the Water Accounting Model 2 layers, we examine the annual and seasonal changes in moisture sources and sinks. We find that most of Kenya’s precipitation originates as oceanic evaporation but that 10% of its precipitation originates as evaporation within Kenya. This internal recycling is concentrated in the mountainous and forested Kenyan highlands, with some locations recycling more than 15% of evaporation to Kenyan precipitation. We also find that 75% of Kenyan evaporation falls as precipitation elsewhere over land, including 10% in Kenya, 25% in the Democratic Republic of the Congo, and around 5% falling in Tanzania and Uganda. Further, we find a positive relationship between increasing rates of moisture recycling and fractional forest cover within Kenya. By beginning to understand both the seasonal and biophysical interactions taking place, we may begin to understand the types of leverage points that exist for integrated atmospheric water cycle management. These findings have broader implications for disentangling environmental management and conservation and have relevance for large-scale discussions about sustainable development.
Abstract
Achievement of the United Nations Sustainable Development Goals (SDGs) is contingent on understanding the potential interactions among human and natural systems. In Kenya, the goal of conserving and expanding forest cover to achieve SDG 15 “Life on Land” may be related to other SDGs because it plays a role in regulating some aspects of Kenyan precipitation. We present a 40-yr analysis of the sources of precipitation in Kenya and the fate of the evaporation that arises from within Kenya. Using MERRA-2 climate reanalysis and the Water Accounting Model 2 layers, we examine the annual and seasonal changes in moisture sources and sinks. We find that most of Kenya’s precipitation originates as oceanic evaporation but that 10% of its precipitation originates as evaporation within Kenya. This internal recycling is concentrated in the mountainous and forested Kenyan highlands, with some locations recycling more than 15% of evaporation to Kenyan precipitation. We also find that 75% of Kenyan evaporation falls as precipitation elsewhere over land, including 10% in Kenya, 25% in the Democratic Republic of the Congo, and around 5% falling in Tanzania and Uganda. Further, we find a positive relationship between increasing rates of moisture recycling and fractional forest cover within Kenya. By beginning to understand both the seasonal and biophysical interactions taking place, we may begin to understand the types of leverage points that exist for integrated atmospheric water cycle management. These findings have broader implications for disentangling environmental management and conservation and have relevance for large-scale discussions about sustainable development.
Abstract
The middle Rio Grande is a vital source of water for irrigation in the region. Climate change is impacting regional hydrology and is likely to put additional stress on a water supply that is already stretched thin. To gain insight on the hydrologic effects of climate change on reservoir storage, a simple water balance model was used to simulate the Elephant Butte–Caballo Reservoir system (southern New Mexico). The water balance model was forced by hydrologic inputs generated by 97 climate simulations derived from CMIP5 global climate models, coupled to a surface hydrologic model. Results suggest that the percentage of years that reservoir releases satisfy agricultural water rights allocations over the next 50 years (2021–70) will decrease relative to the past 50 years (1971–2020). The modeling also projects an increase in multiyear drought events that hinder reservoir management strategies to maintain high storage levels. In most cases, changes in reservoir inflows from distant upstream snowmelt is projected to have a greater influence on reservoir storage and water availability downstream of the reservoirs than will changes in local evaporation and precipitation from the reservoir surfaces.
Abstract
The middle Rio Grande is a vital source of water for irrigation in the region. Climate change is impacting regional hydrology and is likely to put additional stress on a water supply that is already stretched thin. To gain insight on the hydrologic effects of climate change on reservoir storage, a simple water balance model was used to simulate the Elephant Butte–Caballo Reservoir system (southern New Mexico). The water balance model was forced by hydrologic inputs generated by 97 climate simulations derived from CMIP5 global climate models, coupled to a surface hydrologic model. Results suggest that the percentage of years that reservoir releases satisfy agricultural water rights allocations over the next 50 years (2021–70) will decrease relative to the past 50 years (1971–2020). The modeling also projects an increase in multiyear drought events that hinder reservoir management strategies to maintain high storage levels. In most cases, changes in reservoir inflows from distant upstream snowmelt is projected to have a greater influence on reservoir storage and water availability downstream of the reservoirs than will changes in local evaporation and precipitation from the reservoir surfaces.
Abstract
Tropical cyclones (TCs) routinely transport organisms at their centers of circulation. The TC center of circulation is also often marked by an inversion, and the height of the inversion base may change as the TC intensifies or weakens. In this study, a dataset of 49 dropsonde-measured inversions in 20 separate Atlantic Ocean TCs is compared with spatiotemporally collocated polarimetric radar measurements of bioscatter. Bioscatter signature maximum altitude is found to be a function of temperature lapse rate across the inversion base (r = 0.473), and higher inversion bases were generally associated with denser bioscatter signatures, especially when strong hurricanes (minimum pressure < 950 hPa) were considered (r = 0.601). Characteristics of the bioscatter signature had some skill in predicting TC inversion characteristics (adjusted r 2 of 16%–40%), although predictability was increased when TC intensity was also included as a predictor (adjusted r 2 of 40%–59%). These results indicate promise for using the bioscatter signature to monitor the TC inversion and represent an example of a situation in which the behavior of organisms in the airspace may be indicative of ongoing atmospheric processes.
Significance Statement
Tropical cyclone centers of circulation are often associated with an inversion, the base of which changes altitude with system strengthening and weakening. They may also contain a radar-observable bioscatter signature. In this study, we wanted to determine how the bioscatter signature relates to inversion characteristics for the benefit of meteorologists and biologists. Bioscatter signature characteristics were related to strength of the temperature and dewpoint lapse rates across the inversion base, and deeper/denser bioscatter signatures were typically associated with higher inversion bases. The findings suggest that trends in tropical cyclone inversion characteristics could be remotely monitored via the bioscatter signature. They also support prior speculation that some birds may seek the relatively laminar flow above an inversion base.
Abstract
Tropical cyclones (TCs) routinely transport organisms at their centers of circulation. The TC center of circulation is also often marked by an inversion, and the height of the inversion base may change as the TC intensifies or weakens. In this study, a dataset of 49 dropsonde-measured inversions in 20 separate Atlantic Ocean TCs is compared with spatiotemporally collocated polarimetric radar measurements of bioscatter. Bioscatter signature maximum altitude is found to be a function of temperature lapse rate across the inversion base (r = 0.473), and higher inversion bases were generally associated with denser bioscatter signatures, especially when strong hurricanes (minimum pressure < 950 hPa) were considered (r = 0.601). Characteristics of the bioscatter signature had some skill in predicting TC inversion characteristics (adjusted r 2 of 16%–40%), although predictability was increased when TC intensity was also included as a predictor (adjusted r 2 of 40%–59%). These results indicate promise for using the bioscatter signature to monitor the TC inversion and represent an example of a situation in which the behavior of organisms in the airspace may be indicative of ongoing atmospheric processes.
Significance Statement
Tropical cyclone centers of circulation are often associated with an inversion, the base of which changes altitude with system strengthening and weakening. They may also contain a radar-observable bioscatter signature. In this study, we wanted to determine how the bioscatter signature relates to inversion characteristics for the benefit of meteorologists and biologists. Bioscatter signature characteristics were related to strength of the temperature and dewpoint lapse rates across the inversion base, and deeper/denser bioscatter signatures were typically associated with higher inversion bases. The findings suggest that trends in tropical cyclone inversion characteristics could be remotely monitored via the bioscatter signature. They also support prior speculation that some birds may seek the relatively laminar flow above an inversion base.
Abstract
Rural areas are increasingly subject to the effects of telecouplings (socioeconomic and environmental interactions over distances) whereby their human and natural dynamics are linked to socioeconomic and environmental drivers operating far away, such as the growing demand for labor and ecosystem services in cities. Although there have been many studies evaluating the effects of telecouplings, telecouplings in those studies were often investigated separately, and how telecouplings may interact and affect dynamics of rural coupled human and natural systems (CHANS) jointly was rarely evaluated. In this study, we developed an agent-based model and simulated the impacts of two globally common telecouplings, nature-based tourism and labor migration, on forest dynamics of a rural CHANS, China’s Wolong Nature Reserve (Wolong). Nature-based tourism and labor migration can facilitate forest recovery, and the predicted forest areas in Wolong in 2030 would be reduced by 26.2 km2 (6.8%) and 23.9 km2 (6.2%), respectively, without their effects. However, tourism development can significantly reduce the probability of local households to have member(s) outmigrate to work in cities and decrease the positive impact of labor migration on forest recovery. Our simulations show that the interaction between tourism and labor migration can reduce the potential forest recovery by 3.5 km2 (5.0%) in 2030. Our study highlights that interactions among different telecouplings can generate significant impacts on socioeconomic and environmental outcomes and should be jointly considered in the design, management, and evaluation of telecouplings for achieving sustainable development goals.
Significance Statement
Rural areas are increasingly connected with other places through telecouplings, such as tourism and labor migration. However, telecouplings’ effects were often evaluated separately, and their interaction remains poorly understood. In this study, we evaluated how two globally common telecouplings, tourism and labor migration, jointly affect forest dynamics in a demonstration site using an agent-based modeling approach. Although both tourism and labor migration can benefit forest conservation, we found that their interaction generates an antagonistic effect: households’ involvement in tourism activities reduces their probability to have members outmigrate to work in cities and significantly diminishes the beneficial impact of labor migration on forest recovery. Our study highlights the importance of considering interaction among telecouplings in the management of telecouplings for sustainability.
Abstract
Rural areas are increasingly subject to the effects of telecouplings (socioeconomic and environmental interactions over distances) whereby their human and natural dynamics are linked to socioeconomic and environmental drivers operating far away, such as the growing demand for labor and ecosystem services in cities. Although there have been many studies evaluating the effects of telecouplings, telecouplings in those studies were often investigated separately, and how telecouplings may interact and affect dynamics of rural coupled human and natural systems (CHANS) jointly was rarely evaluated. In this study, we developed an agent-based model and simulated the impacts of two globally common telecouplings, nature-based tourism and labor migration, on forest dynamics of a rural CHANS, China’s Wolong Nature Reserve (Wolong). Nature-based tourism and labor migration can facilitate forest recovery, and the predicted forest areas in Wolong in 2030 would be reduced by 26.2 km2 (6.8%) and 23.9 km2 (6.2%), respectively, without their effects. However, tourism development can significantly reduce the probability of local households to have member(s) outmigrate to work in cities and decrease the positive impact of labor migration on forest recovery. Our simulations show that the interaction between tourism and labor migration can reduce the potential forest recovery by 3.5 km2 (5.0%) in 2030. Our study highlights that interactions among different telecouplings can generate significant impacts on socioeconomic and environmental outcomes and should be jointly considered in the design, management, and evaluation of telecouplings for achieving sustainable development goals.
Significance Statement
Rural areas are increasingly connected with other places through telecouplings, such as tourism and labor migration. However, telecouplings’ effects were often evaluated separately, and their interaction remains poorly understood. In this study, we evaluated how two globally common telecouplings, tourism and labor migration, jointly affect forest dynamics in a demonstration site using an agent-based modeling approach. Although both tourism and labor migration can benefit forest conservation, we found that their interaction generates an antagonistic effect: households’ involvement in tourism activities reduces their probability to have members outmigrate to work in cities and significantly diminishes the beneficial impact of labor migration on forest recovery. Our study highlights the importance of considering interaction among telecouplings in the management of telecouplings for sustainability.
Abstract
Extensive and severe droughts have substantial effects on water supplies, agriculture, and aquatic ecosystems. To better understand these droughts, we used tree-ring-based reconstructions of the Palmer drought severity index (PDSI) for the period 1475–2017 to examine droughts that covered at least 33% of the conterminous United States (CONUS). We identified 37 spatially extensive drought events for the CONUS and examined their spatial and temporal patterns. The duration of the extensive drought events ranged from 3 to 12 yr and on average affected 43% of the CONUS. The recent (2000–08) drought in the southwestern CONUS, often referred to as the turn-of-the-century drought, is likely one of the longest droughts in the CONUS during the past 500 years. A principal components analysis of the PDSI data from 1475 through 2017 resulted in three principal components (PCs) that explain about 48% of the variability of PDSI and are helpful to understand the temporal and spatial variability of the 37 extensive droughts in the CONUS. Analyses of the relations between the three PCs and well-known climate indices, such as indices of El Niño–Southern Oscillation, indicate statistically significant correlations; however, the correlations do not appear to be large enough (all with an absolute value less than 0.45) to be useful for the development of drought prediction models.
Significance Statement
To better understand the variability of spatially extensive U.S. droughts through time and across space, we examined tree-ring-based reconstructions of a relative dryness/wetness index for the period 1475–2017. We identified 37 extensive drought events with durations that ranged from 3 to 12 years and that on average affected 43% of the conterminous United States. Also, three of the seven longest droughts occurred after 1900. Because associations between indices of climatic conditions and drought are weak, use of climatic indices for predictive models of drought seems tenuous.
Abstract
Extensive and severe droughts have substantial effects on water supplies, agriculture, and aquatic ecosystems. To better understand these droughts, we used tree-ring-based reconstructions of the Palmer drought severity index (PDSI) for the period 1475–2017 to examine droughts that covered at least 33% of the conterminous United States (CONUS). We identified 37 spatially extensive drought events for the CONUS and examined their spatial and temporal patterns. The duration of the extensive drought events ranged from 3 to 12 yr and on average affected 43% of the CONUS. The recent (2000–08) drought in the southwestern CONUS, often referred to as the turn-of-the-century drought, is likely one of the longest droughts in the CONUS during the past 500 years. A principal components analysis of the PDSI data from 1475 through 2017 resulted in three principal components (PCs) that explain about 48% of the variability of PDSI and are helpful to understand the temporal and spatial variability of the 37 extensive droughts in the CONUS. Analyses of the relations between the three PCs and well-known climate indices, such as indices of El Niño–Southern Oscillation, indicate statistically significant correlations; however, the correlations do not appear to be large enough (all with an absolute value less than 0.45) to be useful for the development of drought prediction models.
Significance Statement
To better understand the variability of spatially extensive U.S. droughts through time and across space, we examined tree-ring-based reconstructions of a relative dryness/wetness index for the period 1475–2017. We identified 37 extensive drought events with durations that ranged from 3 to 12 years and that on average affected 43% of the conterminous United States. Also, three of the seven longest droughts occurred after 1900. Because associations between indices of climatic conditions and drought are weak, use of climatic indices for predictive models of drought seems tenuous.
Abstract
Rainfall-induced landsliding is a global and systemic hazard that is likely to increase with the projections of increased frequency of extreme precipitation with current climate change. However, our ability to understand and mitigate landslide risk is strongly limited by the availability of relevant rainfall measurements in many landslide prone areas. In the last decade, global satellite multisensor precipitation products (SMPP) have been proposed as a solution, but very few studies have assessed their ability to adequately characterize rainfall events triggering landsliding. Here, we address this issue by testing the rainfall pattern retrieved by two SMPPs (IMERG and GSMaP) and one hybrid product [Multi-Source Weighted-Ensemble Precipitation (MSWEP)] against a large, global database of 20 comprehensive landslide inventories associated with well-identified storm events. We found that, after converting total rainfall amounts to an anomaly relative to the 10-yr return rainfall R*, the three products do retrieve the largest anomaly (of the last 20 years) during the major landslide event for many cases. However, the degree of spatial collocation of R* and landsliding varies from case to case and across products, and we often retrieved R* > 1 in years without reported landsliding. In addition, the few (four) landslide events caused by short and localized storms are most often undetected. We also show that, in at least five cases, the SMPP’s spatial pattern of rainfall anomaly matches landsliding less well than does ground-based radar rainfall pattern or lightning maps, underlining the limited accuracy of the SMPPs. We conclude on some potential avenues to improve SMPPs’ retrieval and their relation to landsliding.
Significance Statement
Rainfall-induced landsliding is a global hazard that is expected to increase as a result of anthropogenic climate change. Our ability to understand and mitigate this hazard is strongly limited by the lack of rainfall measurements in mountainous areas. Here, we perform the first global assessment of the potential of three high-resolution precipitation datasets, derived from satellite observations, to capture the rainfall characteristics of 20 storms that led to widespread landsliding. We find that, accounting for past extreme rainfall statistics (i.e., the rainfall returning every 10 years), most storms causing landslides are retrieved by the datasets. However, the shortest storms (i.e., ∼3 h) are often undetected, and the detailed spatial pattern of extreme rainfall often appears to be distorted. Our work opens new ways to study global landslide hazard but also warns against overinterpreting rainfall derived from satellites.
Abstract
Rainfall-induced landsliding is a global and systemic hazard that is likely to increase with the projections of increased frequency of extreme precipitation with current climate change. However, our ability to understand and mitigate landslide risk is strongly limited by the availability of relevant rainfall measurements in many landslide prone areas. In the last decade, global satellite multisensor precipitation products (SMPP) have been proposed as a solution, but very few studies have assessed their ability to adequately characterize rainfall events triggering landsliding. Here, we address this issue by testing the rainfall pattern retrieved by two SMPPs (IMERG and GSMaP) and one hybrid product [Multi-Source Weighted-Ensemble Precipitation (MSWEP)] against a large, global database of 20 comprehensive landslide inventories associated with well-identified storm events. We found that, after converting total rainfall amounts to an anomaly relative to the 10-yr return rainfall R*, the three products do retrieve the largest anomaly (of the last 20 years) during the major landslide event for many cases. However, the degree of spatial collocation of R* and landsliding varies from case to case and across products, and we often retrieved R* > 1 in years without reported landsliding. In addition, the few (four) landslide events caused by short and localized storms are most often undetected. We also show that, in at least five cases, the SMPP’s spatial pattern of rainfall anomaly matches landsliding less well than does ground-based radar rainfall pattern or lightning maps, underlining the limited accuracy of the SMPPs. We conclude on some potential avenues to improve SMPPs’ retrieval and their relation to landsliding.
Significance Statement
Rainfall-induced landsliding is a global hazard that is expected to increase as a result of anthropogenic climate change. Our ability to understand and mitigate this hazard is strongly limited by the lack of rainfall measurements in mountainous areas. Here, we perform the first global assessment of the potential of three high-resolution precipitation datasets, derived from satellite observations, to capture the rainfall characteristics of 20 storms that led to widespread landsliding. We find that, accounting for past extreme rainfall statistics (i.e., the rainfall returning every 10 years), most storms causing landslides are retrieved by the datasets. However, the shortest storms (i.e., ∼3 h) are often undetected, and the detailed spatial pattern of extreme rainfall often appears to be distorted. Our work opens new ways to study global landslide hazard but also warns against overinterpreting rainfall derived from satellites.
Abstract
Understanding near-surface atmospheric behavior in the tropics is imperative given the role of tropical energy fluxes in Earth’s climate cycles, but this area is complicated by a land–atmosphere interaction that includes rugged topography, seasonal weather drivers, and frequent environmental disturbances. This study examines variation in near-surface atmospheric behaviors in northeastern Puerto Rico using a synthesis of data from lowland and montane locations under different land covers (forest, urban, and rural) during 2008–21, when a severe drought, large hurricanes (Irma and Maria), and the COVID-19 mobility-reducing lockdown occurred. Ceilometer, weather, air quality, radiosonde, and satellite data were analyzed for annual patterns and monthly time series of data and data correlations. The results showed a system that is strongly dominated by easterly trade winds transmitting regional oceanic patterns over terrain. Environmental disturbances affected land–atmosphere interaction for short time periods after events. Events that reduce the land signature (reducing greenness: e.g., drought and hurricanes, or reducing land pollution: e.g., COVID-19 lockdown) were evidenced to strengthen the transmission of the oceanic pattern. The most variation in near-surface atmospheric behavior was seen in the mountainous areas that were influenced by both factors: trade winds, and terrain-induced orographic lifting. As an exception to the rest of the near-surface atmospheric behavior, pollutants other than ozone did not correlate positively or negatively with stronger trade winds at all sites across the region. Instead, these pollutants were hypothesized to be more anthropogenically influenced. Once COVID-19 lockdown had persisted for 3 months, urban pollution decreased and cloud base may have increased.
Abstract
Understanding near-surface atmospheric behavior in the tropics is imperative given the role of tropical energy fluxes in Earth’s climate cycles, but this area is complicated by a land–atmosphere interaction that includes rugged topography, seasonal weather drivers, and frequent environmental disturbances. This study examines variation in near-surface atmospheric behaviors in northeastern Puerto Rico using a synthesis of data from lowland and montane locations under different land covers (forest, urban, and rural) during 2008–21, when a severe drought, large hurricanes (Irma and Maria), and the COVID-19 mobility-reducing lockdown occurred. Ceilometer, weather, air quality, radiosonde, and satellite data were analyzed for annual patterns and monthly time series of data and data correlations. The results showed a system that is strongly dominated by easterly trade winds transmitting regional oceanic patterns over terrain. Environmental disturbances affected land–atmosphere interaction for short time periods after events. Events that reduce the land signature (reducing greenness: e.g., drought and hurricanes, or reducing land pollution: e.g., COVID-19 lockdown) were evidenced to strengthen the transmission of the oceanic pattern. The most variation in near-surface atmospheric behavior was seen in the mountainous areas that were influenced by both factors: trade winds, and terrain-induced orographic lifting. As an exception to the rest of the near-surface atmospheric behavior, pollutants other than ozone did not correlate positively or negatively with stronger trade winds at all sites across the region. Instead, these pollutants were hypothesized to be more anthropogenically influenced. Once COVID-19 lockdown had persisted for 3 months, urban pollution decreased and cloud base may have increased.
Abstract
The Tibetan Plateau (TP) has undergone extreme changes in climatic and land surface conditions that are due to a warming climate and land-cover changes. We examined the change in vegetation dynamics from 1982 to 2015 and explored the associations of vegetation with atmospheric variables over the alpine grasslands in the western TP during May as an early growing season. The linear regression analysis of area-averaged normalized difference vegetation index (NDVI) over the western TP in May demonstrated a 7.5% decrease of NDVI during the period from 1982 to 2015, an increase of NDVI by 11.3% from 1982 to 1998, and a decrease of NDVI by 14.5% from 1999 to 2015. The significantly changed NDVI in the western TP could result in the substantial changes in surface energy balances as shown in the surface climatic variables of albedo, net solar radiation, sensible heat flux, latent heat fluxes, and 2-m temperature. The land and atmosphere associations were not confined to the surface but also extended into the upper-level atmosphere up to the 300-hPa level as indicated by the significant positive associations between NDVI and temperatures in both air temperature and equivalent temperature, resulting in more than a 1-K increase with NDVI. Therefore, we concluded that the increasing or decreasing vegetation cover in the western TP during May can respectively increase or decrease the temperatures near the surface and upper atmosphere through a positive physical linkage among the vegetation cover, surface energy fluxes, and temperatures. The positive energy processes of vegetation with temperature could further amplify the variations of temperature and thus water availability.
Significance Statement
The Tibetan Plateau (TP) is an important landmass that plays a significant role in both regional and global climates. This study aims to examine the vegetation change in the TP during May as an early growing season to examine the changes in the near-surface and upper-level climatic conditions associated with vegetation change and to identify the plausible physical processes of the vegetation effects on atmosphere. The satellite-derived vegetation index showed a 7.5% decrease from 1982 to 2015 in the western TP during May. This study identified the positive associations of vegetation activity with temperature and proposed a positive energy process for land–atmosphere interactions over the alpine grasslands in the western region of TP during the transition period from winter to spring.
Abstract
The Tibetan Plateau (TP) has undergone extreme changes in climatic and land surface conditions that are due to a warming climate and land-cover changes. We examined the change in vegetation dynamics from 1982 to 2015 and explored the associations of vegetation with atmospheric variables over the alpine grasslands in the western TP during May as an early growing season. The linear regression analysis of area-averaged normalized difference vegetation index (NDVI) over the western TP in May demonstrated a 7.5% decrease of NDVI during the period from 1982 to 2015, an increase of NDVI by 11.3% from 1982 to 1998, and a decrease of NDVI by 14.5% from 1999 to 2015. The significantly changed NDVI in the western TP could result in the substantial changes in surface energy balances as shown in the surface climatic variables of albedo, net solar radiation, sensible heat flux, latent heat fluxes, and 2-m temperature. The land and atmosphere associations were not confined to the surface but also extended into the upper-level atmosphere up to the 300-hPa level as indicated by the significant positive associations between NDVI and temperatures in both air temperature and equivalent temperature, resulting in more than a 1-K increase with NDVI. Therefore, we concluded that the increasing or decreasing vegetation cover in the western TP during May can respectively increase or decrease the temperatures near the surface and upper atmosphere through a positive physical linkage among the vegetation cover, surface energy fluxes, and temperatures. The positive energy processes of vegetation with temperature could further amplify the variations of temperature and thus water availability.
Significance Statement
The Tibetan Plateau (TP) is an important landmass that plays a significant role in both regional and global climates. This study aims to examine the vegetation change in the TP during May as an early growing season to examine the changes in the near-surface and upper-level climatic conditions associated with vegetation change and to identify the plausible physical processes of the vegetation effects on atmosphere. The satellite-derived vegetation index showed a 7.5% decrease from 1982 to 2015 in the western TP during May. This study identified the positive associations of vegetation activity with temperature and proposed a positive energy process for land–atmosphere interactions over the alpine grasslands in the western region of TP during the transition period from winter to spring.
Abstract
Parts of southeast Alaska experienced record drought in 2019, followed by record daily precipitation in late 2020 with substantial impacts to human health and safety, energy resources, and fisheries. To help ascertain whether these types of events can be expected more frequently, this study investigated observed trends and projected changes of hydroclimatic extremes indices across southeast Alaska, including measures of precipitation variability, seasonality, magnitude, and type. Observations indicated mixed tendencies of interannual precipitation variability, but there were consistent trends toward warmer and wetter conditions. Projected changes were assessed using dynamically downscaled climate model simulations at 4-km spatial resolution from 2031 to 2060 that were compared with a historical period from 1981 to 2010 using two models—NCAR CCSM4 and GFDL CM3. Consistent directional changes were found for five of the analyzed indices. The CCSM indicated increased maximum 1-day precipitation (RX1; 12.6%), increased maximum consecutive 5-day precipitation (RX5; 7.4%), longer periods of consecutive dry days (CDD; 11.9%), fewer snow cover days (SNC; −21.4%) and lower snow fraction (SNF; −24.4%); for GFDL these changes were 19.8% for RX1, 16.0% for RX5, 20.1% for CDD, −21.9% for SNC, and −26.5% for SNF. Although both models indicated substantial snow losses, they also projected annual snowfall increases at high elevations; this occurred above 1500 m for CCSM and above 2500 m for GFDL. Significance testing was assessed at the 95% confidence level using Theil–Sen’s slope estimates for the observed time series and the Wilcoxon–Mann–Whitney U test for projected changes of the hydroclimatic extremes indices relative to their historical distributions.
Abstract
Parts of southeast Alaska experienced record drought in 2019, followed by record daily precipitation in late 2020 with substantial impacts to human health and safety, energy resources, and fisheries. To help ascertain whether these types of events can be expected more frequently, this study investigated observed trends and projected changes of hydroclimatic extremes indices across southeast Alaska, including measures of precipitation variability, seasonality, magnitude, and type. Observations indicated mixed tendencies of interannual precipitation variability, but there were consistent trends toward warmer and wetter conditions. Projected changes were assessed using dynamically downscaled climate model simulations at 4-km spatial resolution from 2031 to 2060 that were compared with a historical period from 1981 to 2010 using two models—NCAR CCSM4 and GFDL CM3. Consistent directional changes were found for five of the analyzed indices. The CCSM indicated increased maximum 1-day precipitation (RX1; 12.6%), increased maximum consecutive 5-day precipitation (RX5; 7.4%), longer periods of consecutive dry days (CDD; 11.9%), fewer snow cover days (SNC; −21.4%) and lower snow fraction (SNF; −24.4%); for GFDL these changes were 19.8% for RX1, 16.0% for RX5, 20.1% for CDD, −21.9% for SNC, and −26.5% for SNF. Although both models indicated substantial snow losses, they also projected annual snowfall increases at high elevations; this occurred above 1500 m for CCSM and above 2500 m for GFDL. Significance testing was assessed at the 95% confidence level using Theil–Sen’s slope estimates for the observed time series and the Wilcoxon–Mann–Whitney U test for projected changes of the hydroclimatic extremes indices relative to their historical distributions.
