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

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.

Abstract

Over a two-year period beginning in 2015, a panel of subject matter experts, the Space Platform Requirements Working Group (SPRWG), carried out an analysis and prioritization of different space-based observations supporting the National Oceanic and Atmospheric Administration (NOAA)’s operational services in the areas of weather, oceans, and space weather. NOAA leadership used the SPRWG analysis of space-based observational priorities in different mission areas, among other inputs, to inform the Multi-Attribute Utility Theory (MAUT)-based value model and the NOAA Satellite Observing Systems Architecture (NSOSA) study. The goal of the NSOSA study is to develop candidate satellite architectures for the era beginning in approximately 2030. The SPRWG analysis included a prioritized list of observational objectives together with the quantitative attributes of each objective at three levels of performance: a threshold level of minimal utility, an intermediate level that the community expects by 2030, and a maximum effective level, a level for which further improvements would not be cost effective. This process is believed to be unprecedented in the analysis of long-range plans for providing observations from space. This paper describes the process for developing the prioritized objectives and their attributes and how they were combined in the Environmental Data Record (EDR) Value Model (EVM). The EVM helped inform NOAA’s assessment of many potential architectures for its future observing system within the NSOSA study. However, neither the SPRWG nor its report represents official NOAA policy positions or decisions, and the responsibility for selecting and implementing the final architecture rests solely with NOAA senior leadership.

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO₂) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO₂ concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.

CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.

Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.