Regional climate change projections for the Northeast USA

Hayhoe, Katharine; Wake, Cameron P.; Anderson, Bruce T.; Liang, Xin‐Zhong; Maurer, Edwin P.; Zhu, Jiamin; Bradbury, James A.; DeGaetano, Art; Stoner, Anne M. K.; Wuebbles, Donald J.

doi:10.1007/s11027-007-9133-2

Cited by 231 publications

(180 citation statements)

References 31 publications

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“…Our motivation for this is to provide fair comparisons against the forcing GCM, which (as noted in Hayhoe et al 2007) was never meant to simulate fine-scale features. Additionally, since widespread small-scale errors project onto larger-scale averages, this approach provides spatially-robust results in a compact format.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a WRF dynamical downscaling simulation over California

et al. 2009

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This paper presents results from a 40 year Weather Research and Forecasting (WRF) based dynamical downscaling experiment performed at 12 km horizontal grid spacing, centered on the state of California, and forced by a 1 • × 1.25 • finite-volume current-climate Community Climate System Model ver. 3 (CCSM3) simulation. In-depth comparisons between modeled and observed regional-average precipitation, 2 m temperature, and snowpack are performed. The regional model reproduces the spatial distribution of precipitation quite well, but substantially overestimates rainfall along windward slopes. This is due to strong overprediction of precipitation intensity; precipitation frequency is actually underpredicted by the model. Moisture fluxes impinging on the coast seem to be well-represented over California, implying that precipitation bias is caused by processes internal to WRF. Positive-definite moisture advection and use of the Grell cumulus parameterization result in some decrease in precipitation bias, but other sources are needed to explain the full bias magnitude. Surface temperature is well simulated in all seasons except summer, when overly-dry soil moisture results in a several degree warm bias in both CCSM3 and WRF. Additionally, coastal temperatures appear to be too warm due to a coastal sea surface temperature bias inherited from CCSM3. Modeled snowfall/snowmelt agrees quite well with observations, but snow water equivalent is found to be much too low due to monthly reinitialization of all regional model fields from CCSM3 values.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a WRF dynamical downscaling simulation over California

et al. 2009

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“…Also, a number of new results have been published; RCMs can resolve features down to 50 km or less and many regional high-resolution (about 25 km) projections of future climate change have been developed (Hayhoe et al 2008;Elguindi and Grundstein 2012;Patricola and Cook 2010). The Earth System Grid Federation (ESGF) is a coordinated international collaboration of individuals and institutions that is developing, deploying and maintaining software infrastructure for the management of model output and observational data.…”

Section: Methodsmentioning

confidence: 99%

A Geographic Information System (GIS)-based approach to adaptation to regional climate change: a case study of Okutama-machi, Tokyo, Japan

Bai

Kaneko

Kobayashi

et al. 2013

Mitig Adapt Strateg Glob Change

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Recently, local governments have an increasing need to take extensive and effective local measures to adapt to regional climate change, but have difficulty knowing how and when to adapt to such change. This study aims: 1) to characterize an efficient and cost-effective database management tool (DMT) for developing a Geographic Information System (GIS) based approach to using observed and projected data, for decision-making by non-expert government authorities, and 2) to document how DMT can be used to provide specialized yet understandable climate change information to assist local decision-makers in clarifying regional priorities within a Mitig Adapt Strateg Glob Change (2014) wide array of adaptation options. The DMT combines climate change mapping, statistical GIS, and a vulnerability assessment. Okutama-machi, a 225.63 km 2 sparsely populated mountainous region (2012 population 5,856) northwest of Tokyo, Japan, was chosen for this pilot study. In this paper, the most recent regional climate projections (5 km resolution) are transcribed into an understandable form for use by non-expert citizens who use the GIS-based DMT. Results illustrate qualitative agreement in projection of summer daily mean temperatures; the mean temperature increase at Okutama-machi is the greatest of any area in Tokyo. In comparing near future and future conditions, August monthly mean temperature will increase more than 0.7-0.9°C and 2.8-2.9°C, and monthly precipitation by 50 % and 25-41 %, respectively. However, the root mean square (RMS) errors and bias of percentage change for monthly precipitation in summertime are 26.8 % and 4.3 %, respectively. These data provide an early warning and have implications for local climate policy response.

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“…We estimated the overlap between historical and end-of-century (2070-2099) climates in the northeastern United States using v www.esajournals.org data from three atmospheric-ocean-general-circulation-models (AOGCMs) statistically downscaled to a 0.1258 resolution (;13 km; Hayhoe et al 2008) and forced with the A1fi emissions scenario (Fig. 1b).…”

Section: Mapping the Vulnerability Of Biodiversitymentioning

confidence: 99%

Climate‐smart management of biodiversity

2015

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Abstract. Determining where biodiversity is likely to be most vulnerable to climate change and methods to reduce that vulnerability are necessary first steps to incorporate climate change into biodiversity management plans. Here, we use a spatial climate change vulnerability assessment to (1) map the potential vulnerability of terrestrial biodiversity to climate change in the northeastern United States and (2) provide guidance on how and where management actions for biodiversity could provide long-term benefits under climate change (i.e., climate-smart management considerations). Our model suggests that biodiversity will be most vulnerable in Delaware, Maryland, and the District of Columbia due to the combination of high climate change velocity, high landscape resistance, and high topoclimate homogeneity. Biodiversity is predicted to be least vulnerable in Vermont, Maine, and New Hampshire because large portions of these states have low landscape resistance, low climate change velocity, and low topoclimate homogeneity. Our spatial climate-smart management considerations suggest that: (1) high topoclimate diversity could moderate the effects of climate change across 50% of the region; (2) decreasing local landscape resistance in conjunction with other management actions could increase the benefit of those actions across 17% of the region; and (3) management actions across 24% of the region could provide long-term benefits by promoting short-term population persistence that provides a source population capable of moving in the future. The guidance and framework we provide here should allow conservation organizations to incorporate our climate-smart management considerations into management plans without drastically changing their approach to biodiversity conservation.

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Regional climate change projections for the Northeast USA

Abstract: Climate change, Downscaling, General circulation model, Greenhouse gas emission scenarios, Northeast United States,

Cited by 231 publications

References 31 publications

Evaluation of a WRF dynamical downscaling simulation over California

Evaluation of a WRF dynamical downscaling simulation over California

A Geographic Information System (GIS)-based approach to adaptation to regional climate change: a case study of Okutama-machi, Tokyo, Japan

Climate‐smart management of biodiversity

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