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- Author or Editor: Cynthia Rosenzweig x
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Abstract
A land-surface model is linked to the Goddard Institute for Space Studies (GISS) GCM Model II to form Model II-LS. The land-surface model primarily influences the simulation of surface air temperature over land, both in monthly means and diurnal range, and affects the major components of the hydrologic cycle over land—evapotranspiration, runoff, and, more indirectly, precipitation. Comparisons of January and July results of Model II-LS to results generated from the GISS GCM Model II and to observations show that the new land surface primarily provides improvements in the simulation of global evaporation and diurnal surface temperature range. The interannual variability of June–August surface air temperature in the Northern Hemisphere is also improved. When the land-surface model is combined with new parameterizations for moist convection and the planetary boundary layer, the combined version of the GCM yields improvements in evaporation. Simulations of a grassland site from an off-line version of the land-surface model compared to observations provided by the Project for Intercomparison of Land-Surface Parameterization Schemes of the Global Energy and Water Cycle Experiment show that the land-surface model simulates the ground temperature lag with depth in a reasonable way. Thus, the land-surface parameterization provides a more realistic simulation of climate variables over land in conjunction with other improvements to the GISS GCM.
Abstract
A land-surface model is linked to the Goddard Institute for Space Studies (GISS) GCM Model II to form Model II-LS. The land-surface model primarily influences the simulation of surface air temperature over land, both in monthly means and diurnal range, and affects the major components of the hydrologic cycle over land—evapotranspiration, runoff, and, more indirectly, precipitation. Comparisons of January and July results of Model II-LS to results generated from the GISS GCM Model II and to observations show that the new land surface primarily provides improvements in the simulation of global evaporation and diurnal surface temperature range. The interannual variability of June–August surface air temperature in the Northern Hemisphere is also improved. When the land-surface model is combined with new parameterizations for moist convection and the planetary boundary layer, the combined version of the GCM yields improvements in evaporation. Simulations of a grassland site from an off-line version of the land-surface model compared to observations provided by the Project for Intercomparison of Land-Surface Parameterization Schemes of the Global Energy and Water Cycle Experiment show that the land-surface model simulates the ground temperature lag with depth in a reasonable way. Thus, the land-surface parameterization provides a more realistic simulation of climate variables over land in conjunction with other improvements to the GISS GCM.
Abstract
The current generation of land-surface models used in GCMs view the soil column as the fundamental hydrologic unit. While this may be effective in simulating such processes as the evolution of ground temperatures and the growth/ablation of a snowpack at the soil plot scale, it effectively ignores the role topography plays in the development of soil moisture heterogeneity and the subsequent impacts of this soil moisture heterogeneity on watershed evapotranspiration and the partitioning of surface fluxes. This view also ignores the role topography plays in the timing of discharge and the partitioning of discharge into surface runoff and baseflow. In this paper an approach to land-surface modeling is presented that allows us to view the watershed as the fundamental hydrologic unit. The analytic form of TOPMODEL equations are incorporated into the soil column framework and the resulting model is used to predict the saturated fraction of the watershed and baseflow in a consistent fashion. Soil moisture heterogeneity represented by saturated lowlands subsequently impacts the partitioning of surface fluxes, including evapotranspiration and runoff. The approach is computationally efficient, allows for a greatly improved simulation of the hydrologic cycle, and is easily coupled into the existing framework of the current generation of single column land-surface models. Because this approach uses the statistics of the topography rather than the details of the topography, it is compatible with the large spatial scales of today’s regional and global climate models. Five years of meteorological and hydrological data from the Sleepers River watershed located in the northeastern United States where winter snow cover is significant were used to drive the new model. Site validation data were sufficient to evaluate model performance with regard to various aspects of the watershed water balance, including snowpack growth/ablation, the spring snowmelt hydrograph, storm hydrographs, and the seasonal development of watershed evapotranspiration and soil moisture.
Abstract
The current generation of land-surface models used in GCMs view the soil column as the fundamental hydrologic unit. While this may be effective in simulating such processes as the evolution of ground temperatures and the growth/ablation of a snowpack at the soil plot scale, it effectively ignores the role topography plays in the development of soil moisture heterogeneity and the subsequent impacts of this soil moisture heterogeneity on watershed evapotranspiration and the partitioning of surface fluxes. This view also ignores the role topography plays in the timing of discharge and the partitioning of discharge into surface runoff and baseflow. In this paper an approach to land-surface modeling is presented that allows us to view the watershed as the fundamental hydrologic unit. The analytic form of TOPMODEL equations are incorporated into the soil column framework and the resulting model is used to predict the saturated fraction of the watershed and baseflow in a consistent fashion. Soil moisture heterogeneity represented by saturated lowlands subsequently impacts the partitioning of surface fluxes, including evapotranspiration and runoff. The approach is computationally efficient, allows for a greatly improved simulation of the hydrologic cycle, and is easily coupled into the existing framework of the current generation of single column land-surface models. Because this approach uses the statistics of the topography rather than the details of the topography, it is compatible with the large spatial scales of today’s regional and global climate models. Five years of meteorological and hydrological data from the Sleepers River watershed located in the northeastern United States where winter snow cover is significant were used to drive the new model. Site validation data were sufficient to evaluate model performance with regard to various aspects of the watershed water balance, including snowpack growth/ablation, the spring snowmelt hydrograph, storm hydrographs, and the seasonal development of watershed evapotranspiration and soil moisture.
Abstract
This paper describes a time-sensitive approach to climate change projections that was developed as part of New York City’s climate change adaptation process and that has provided decision support to stakeholders from 40 agencies, regional planning associations, and private companies. The approach optimizes production of projections given constraints faced by decision makers as they incorporate climate change into long-term planning and policy. New York City stakeholders, who are well versed in risk management, helped to preselect the climate variables most likely to impact urban infrastructure and requested a projection range rather than a single “most likely” outcome. The climate projections approach is transferable to other regions and is consistent with broader efforts to provide climate services, including impact, vulnerability, and adaptation information. The approach uses 16 GCMs and three emissions scenarios to calculate monthly change factors based on 30-yr average future time slices relative to a 30-yr model baseline. Projecting these model mean changes onto observed station data for New York City yields dramatic changes in the frequency of extreme events such as coastal flooding and dangerous heat events. On the basis of these methods, the current 1-in-10-year coastal flood is projected to occur more than once every 3 years by the end of the century and heat events are projected to approximately triple in frequency. These frequency changes are of sufficient magnitude to merit consideration in long-term adaptation planning, even though the precise changes in extreme-event frequency are highly uncertain.
Abstract
This paper describes a time-sensitive approach to climate change projections that was developed as part of New York City’s climate change adaptation process and that has provided decision support to stakeholders from 40 agencies, regional planning associations, and private companies. The approach optimizes production of projections given constraints faced by decision makers as they incorporate climate change into long-term planning and policy. New York City stakeholders, who are well versed in risk management, helped to preselect the climate variables most likely to impact urban infrastructure and requested a projection range rather than a single “most likely” outcome. The climate projections approach is transferable to other regions and is consistent with broader efforts to provide climate services, including impact, vulnerability, and adaptation information. The approach uses 16 GCMs and three emissions scenarios to calculate monthly change factors based on 30-yr average future time slices relative to a 30-yr model baseline. Projecting these model mean changes onto observed station data for New York City yields dramatic changes in the frequency of extreme events such as coastal flooding and dangerous heat events. On the basis of these methods, the current 1-in-10-year coastal flood is projected to occur more than once every 3 years by the end of the century and heat events are projected to approximately triple in frequency. These frequency changes are of sufficient magnitude to merit consideration in long-term adaptation planning, even though the precise changes in extreme-event frequency are highly uncertain.
Abstract
Integrating climate risk information into resilience-building activities in the field is important to ensure that adaptation is based on the best available science. Despite this, many challenges exist when developing, communicating, and incorporating climate risk information. There are limited resources on how stakeholders perceive risks, use risk information, and what barriers exist to limit knowledge integration. This paper seeks to define the following: 1) What do conservation stakeholders consider to be the most significant climate risks they face now and possibly in the future? 2) What have been the most significant barriers to their using climate risk information? 3) What sources and types of knowledge would be most useful for these managers to overcome these barriers? A survey was conducted among stakeholders (n = 224) associated with World Wildlife Fund projects in tropical and subtropical countries. A very high proportion of stakeholders used climate risk information and yet faced integration-related challenges, which included too much uncertainty and the lack of a relevant scale for planning. The main factors preventing the use of climate risk information in decision-making were unavailability of climate risk information, no or limited financial or human resources available to respond, lack of organizational mandate or support, and no or limited institutional incentives. Comparing perceived current and future risks revealed a decline in concern for some future climate hazards. Survey respondents identified scientific reports, climate scientists, and online sources as the most useful information sources of climate risk information, while (i) maps and illustrations; (ii) scenarios format; and (iii) data tables, graphs, and charts were identified as user-friendly formats.
Abstract
Integrating climate risk information into resilience-building activities in the field is important to ensure that adaptation is based on the best available science. Despite this, many challenges exist when developing, communicating, and incorporating climate risk information. There are limited resources on how stakeholders perceive risks, use risk information, and what barriers exist to limit knowledge integration. This paper seeks to define the following: 1) What do conservation stakeholders consider to be the most significant climate risks they face now and possibly in the future? 2) What have been the most significant barriers to their using climate risk information? 3) What sources and types of knowledge would be most useful for these managers to overcome these barriers? A survey was conducted among stakeholders (n = 224) associated with World Wildlife Fund projects in tropical and subtropical countries. A very high proportion of stakeholders used climate risk information and yet faced integration-related challenges, which included too much uncertainty and the lack of a relevant scale for planning. The main factors preventing the use of climate risk information in decision-making were unavailability of climate risk information, no or limited financial or human resources available to respond, lack of organizational mandate or support, and no or limited institutional incentives. Comparing perceived current and future risks revealed a decline in concern for some future climate hazards. Survey respondents identified scientific reports, climate scientists, and online sources as the most useful information sources of climate risk information, while (i) maps and illustrations; (ii) scenarios format; and (iii) data tables, graphs, and charts were identified as user-friendly formats.
Abstract
A new approach to simulating the urban environment with a mesocale model has been developed to identify efficient strategies for mitigating increases in surface air temperatures associated with the urban heat island (UHI). A key step in this process is to define a “global” roughness for the cityscape and to use this roughness to diagnose 10-m temperature, moisture, and winds within an atmospheric model. This information is used to calculate local exchange coefficients for different city surface types (each with their own “local roughness” lengths); each surface’s energy balances, including surface air temperatures, humidity, and wind, are then readily obtained. The model was run for several summer days in 2001 for the New York City five-county area. The most effective strategy to reduce the surface radiometric and 2-m surface air temperatures was to increase the albedo of the city (impervious) surfaces. However, this caused increased thermal stress at street level, especially noontime thermal stress. As an alternative, the planting of trees reduced the UHI’s adverse effects of high temperatures and also reduced noontime thermal stress on city residents (and would also have reduced cooling energy requirements of small structures). Taking these results together, the analysis suggests that the best mitigation strategy is planting trees at street level and increasing the reflectivity of roofs.
Abstract
A new approach to simulating the urban environment with a mesocale model has been developed to identify efficient strategies for mitigating increases in surface air temperatures associated with the urban heat island (UHI). A key step in this process is to define a “global” roughness for the cityscape and to use this roughness to diagnose 10-m temperature, moisture, and winds within an atmospheric model. This information is used to calculate local exchange coefficients for different city surface types (each with their own “local roughness” lengths); each surface’s energy balances, including surface air temperatures, humidity, and wind, are then readily obtained. The model was run for several summer days in 2001 for the New York City five-county area. The most effective strategy to reduce the surface radiometric and 2-m surface air temperatures was to increase the albedo of the city (impervious) surfaces. However, this caused increased thermal stress at street level, especially noontime thermal stress. As an alternative, the planting of trees reduced the UHI’s adverse effects of high temperatures and also reduced noontime thermal stress on city residents (and would also have reduced cooling energy requirements of small structures). Taking these results together, the analysis suggests that the best mitigation strategy is planting trees at street level and increasing the reflectivity of roofs.
This study of New York City, New York's, heat island and its potential mitigation was structured around research questions developed by project stakeholders working with a multidisciplinary team of researchers. Meteorological, remotely-sensed, and spatial data on the urban environment were brought together to understand multiple dimensions of New York City's heat island and the feasibility of mitigation strategies, including urban forestry, green roofs, and high-albedo surfaces. Heat island mitigation was simulated with the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5). Results compare the possible effectiveness of mitigation strategies at reducing urban air temperature in six New York City neighborhoods and for New York City as a whole. Throughout the city, the most effective temperature-reduction strategy is to maximize the amount of vegetation, with a combination of tree planting and green roofs. This lowered simulated citywide surface urban air temperature by 0.4°C on average, and 0.7°C at 1500 Eastern Standard Time (EST), when the greatest temperature reductions tend to occur. Decreases of up to 1.1°C at 1500 EST occurred in some neighborhoods in Manhattan and Brooklyn, where there is more available area for implementing vegetation planting. New York City agencies are using project results to guide ongoing urban greening initiatives, particularly tree-planting programs.
This study of New York City, New York's, heat island and its potential mitigation was structured around research questions developed by project stakeholders working with a multidisciplinary team of researchers. Meteorological, remotely-sensed, and spatial data on the urban environment were brought together to understand multiple dimensions of New York City's heat island and the feasibility of mitigation strategies, including urban forestry, green roofs, and high-albedo surfaces. Heat island mitigation was simulated with the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5). Results compare the possible effectiveness of mitigation strategies at reducing urban air temperature in six New York City neighborhoods and for New York City as a whole. Throughout the city, the most effective temperature-reduction strategy is to maximize the amount of vegetation, with a combination of tree planting and green roofs. This lowered simulated citywide surface urban air temperature by 0.4°C on average, and 0.7°C at 1500 Eastern Standard Time (EST), when the greatest temperature reductions tend to occur. Decreases of up to 1.1°C at 1500 EST occurred in some neighborhoods in Manhattan and Brooklyn, where there is more available area for implementing vegetation planting. New York City agencies are using project results to guide ongoing urban greening initiatives, particularly tree-planting programs.
A partnership between Earth scientists and institutional stewards is helping the National Aeronautics and Space Administration (NASA) prepare for a changing climate and growing climate-related vulnerabilities. An important part of this partnership is an agency-wide Climate Adaptation Science Investigator (CASI) Workgroup. CASI has thus far initiated 1) local workshops to introduce and improve planning for climate risks, 2) analysis of climate data and projections for each NASA Center, 3) climate impact and adaptation toolsets, and 4) Center-specific research and engagement.
Partnering scientists with managers aligns climate expertise with operations, leveraging research capabilities to improve decision-making and to tailor risk assessment at the local level. NASA has begun to institutionalize this ongoing process for climate risk management across the entire agency, and specific adaptation strategies are already being implemented.
A case study from Kennedy Space Center illustrates the CASI and workshop process, highlighting the need to protect launch infrastructure of strategic importance to the United States, as well as critical natural habitat. Unique research capabilities and a culture of risk management at NASA may offer a pathway for other organizations facing climate risks, promoting their resilience as part of community, regional, and national strategies.
A partnership between Earth scientists and institutional stewards is helping the National Aeronautics and Space Administration (NASA) prepare for a changing climate and growing climate-related vulnerabilities. An important part of this partnership is an agency-wide Climate Adaptation Science Investigator (CASI) Workgroup. CASI has thus far initiated 1) local workshops to introduce and improve planning for climate risks, 2) analysis of climate data and projections for each NASA Center, 3) climate impact and adaptation toolsets, and 4) Center-specific research and engagement.
Partnering scientists with managers aligns climate expertise with operations, leveraging research capabilities to improve decision-making and to tailor risk assessment at the local level. NASA has begun to institutionalize this ongoing process for climate risk management across the entire agency, and specific adaptation strategies are already being implemented.
A case study from Kennedy Space Center illustrates the CASI and workshop process, highlighting the need to protect launch infrastructure of strategic importance to the United States, as well as critical natural habitat. Unique research capabilities and a culture of risk management at NASA may offer a pathway for other organizations facing climate risks, promoting their resilience as part of community, regional, and national strategies.
