Abstract It is well known that urban areas are typically hotter than the surrounding (vegetated) rural areas. However, the contribution of urbanization to the trends of extreme temperature events such as heat waves (HWs) is less understood. Using a homogenized meteorological dataset drawn from nearly 2,000 stations in China, we find that urban and rural areas have different HW trends and the urban‐rural contrast of HW trends varies across climate regimes. In wet climates, the increasing trends of HWs in urban areas are greater than those in rural areas, suggesting a positive contribution of urbanization to HW trends. In arid regions, the urbanization contribution to HW trends is smaller and even negative. The stronger urbanization contribution to HW trends in wet climates is linked to the smaller variability of urban heat island intensity. This study highlights the important role of local hydroclimate in modulating the urbanization contribution to extreme temperatures.
Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Other. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Vulnerability in a Tropical Cyclone Risk Model: Philippines Case StudyAuthorsJane W.BaldwinChia-YingLeeBrianWalshSuzanaCamargoAdamSobeliDSee all authors Jane W. BaldwinCorresponding Author• Submitting AuthorDepartment of Earth System Science, University of California IrvineLamont-Doherty Earth Observatory, Columbia Universityview email addressThe email was not providedcopy email addressChia-Ying LeeLamont-Doherty Earth Observatory, Columbia Universityview email addressThe email was not providedcopy email addressBrian WalshWorld Bankview email addressThe email was not providedcopy email addressSuzana CamargoLamont-Doherty Earth Observatory, Columbia Universityview email addressThe email was not providedcopy email addressAdam SobeliDColumbia UniversityiDhttps://orcid.org/0000-0003-3602-0567view email addressThe email was not providedcopy email address
Abstract The authors describe a tropical cyclone risk model for the Philippines using open-source methods that can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally forced tropical cyclone hazard model, are combined with data representing exposed value and vulnerability to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is distributed across a country following population density and night-lights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel, with free parameters chosen by a sensitivity analysis in which simulated and historical reported damages are compared for different parameter values and further constrained by information from household surveys about regional building characteristics. Use of different vulnerability parameters for the region around Manila, Philippines, yields much better agreement between simulated and actually reported losses than does a single set of parameters for the entire country. Despite the improvements from regionally refined vulnerability, the model predicts no losses for a substantial number of destructive historical storms, a difference the authors hypothesize is due to the use of wind speed as the sole metric of tropical cyclone hazard, omitting explicit representation of storm surge and/or rainfall. Bearing these limitations in mind, this model can be used to estimate return levels for tropical cyclone–caused wind hazards and asset losses for regions across the Philippines, relevant to some disaster risk reduction and management tasks; this model also provides a platform for further development of open-source tropical cyclone risk modeling. Significance Statement Landfalling tropical cyclones are devastating disasters for which the Philippines is particularly at risk. Here we develop a model for tropical cyclone risk, quantified as property losses, over the Philippines and demonstrate its effectiveness by comparing to historical damages. We find that capturing the difference in vulnerability between the largest city in the Philippines (Manila) and more rural areas is important to accurately represent this risk. Using this model, we can more accurately constrain the risk of very extreme tropical cyclone events in the Philippines. The model can also be straightforwardly adapted for emergency planning in other countries and for climate change scenarios using openly available information.
Abstract Global Climate Models (GCMs) exhibit substantial biases in their simulation of tropical climate. One particularly problematic bias exists in GCMs' simulation of the tropical rainband known as the Intertropical Convergence Zone (ITCZ). Much of the precipitation on Earth falls within the ITCZ, which plays a key role in setting Earth's temperature by affecting global energy transports, and partially dictates dynamics of the largest interannual mode of climate variability: The El Niño‐Southern Oscillation (ENSO). Most GCMs fail to simulate the mean state of the ITCZ correctly, often exhibiting a “double ITCZ bias,” with rainbands both north and south rather than just north of the equator. These tropical mean state biases limit confidence in climate models' simulation of projected future and paleoclimate states, and reduce the utility of these models for understanding present climate dynamics. Adjusting GCM parameterizations of cloud processes and atmospheric convection can reduce tropical biases, as can artificially correcting sea surface temperatures through modifications to air‐sea fluxes (i.e., “flux adjustment”). Here, we argue that a significant portion of these rainfall and circulation biases are rooted in orographic height being biased low due to assumptions made in fitting observed orography onto GCM grids. We demonstrate that making different, and physically defensible, assumptions that raise the orographic height significantly improves model simulation of climatological features such as the ITCZ and North American rainfall as well as the simulation of ENSO. These findings suggest a simple, physically based, and computationally inexpensive method that can improve climate models and projections of future climate.
Abstract Tropical cyclone (TC) risk assessments are critical for disaster preparedness and response. Alongside hazard and exposure, accurate TC risk assessment requires understanding the vulnerability of populations and assets. In this chapter, we examine multiple methods that have been used to assess and quantify TC vulnerability with a focus on open-source methods. We separately discuss structural, economic, and social (or demographic) vulnerability approaches. Structural vulnerability assesses the susceptibility of buildings to be affected by their exposure to hazards; in this section, we provide a detailed overview of how FEMA’s Hazus model quantifies damages by utilizing engineering principles. Economic vulnerability employs regression analysis to relate wind speeds to damages; this discussion explores typical functional forms used to represent vulnerability in such analysis and efforts to constrain parameters in these functions. Finally, social approaches use demographic data to characterize the varying susceptibility of populations to TC risk; we provide some representative examples of this methodology. We conclude with a comparative discussion of these three classes of methods, suggest directions for future work, and ask whether the different approaches can be combined to yield a more holistic view of both the human and structural aspects of TC vulnerability.
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