Analysis of the climate change signal in Mexico City given disagreeing data sources and scattered projections

AbstractIn this work we use a regional atmosphere–ocean coupled model (RAOCM) and its stand-alone atmospheric component to gain insight into the impact of atmosphere–ocean coupling on the climate change signal over the Iberian Peninsula (IP). The IP climate is influenced by both the Atlantic Ocean and the Mediterranean sea. Complex interactions with the orography take place there and high-resolution models are required to realistically reproduce its current and future climate. We find that under the RCP8.5 scenario, the generalized 2-m air temperature (T2M) increase by the end of the twenty-first century (2070–2099) in the atmospheric-only simulation is tempered by the coupling. The impact of coupling is specially seen in summer, when the warming is stronger. Precipitation shows regionally-dependent changes in winter, whilst a drier climate is found in summer. The coupling generally reduces the magnitude of the changes. Differences in T2M and precipitation between the coupled and uncoupled simulations are caused by changes in the Atlantic large-scale circulation and in the Mediterranean Sea. Additionally, the differences in projected changes of T2M and precipitation with the RAOCM under the RCP8.5 and RCP4.5 scenarios are tackled. Results show that in winter and summer T2M increases less and precipitation changes are of a smaller magnitude with the RCP4.5. Whilst in summer changes present a similar regional distribution in both runs, in winter there are some differences in the NW of the IP due to differences in the North Atlantic circulation. The differences in the climate change signal from the RAOCM and the driving Global Coupled Model show that regionalization has an effect in terms of higher resolution over the land and ocean.

Download Full-text

Anthropogenic climate change has changed frequency of past flood during 2010-2013

Progress in Earth and Planetary Science ◽

10.1186/s40645-021-00431-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yukiko Hirabayashi ◽

Haireti Alifu ◽

Dai Yamazaki ◽

Yukiko Imada ◽

Hideo Shiogama ◽

...

Keyword(s):

Climate Change ◽

South America ◽

Radiative Forcing ◽

Water Cycle ◽

Flood Frequency ◽

Anthropogenic Climate Change ◽

Climate Change Signal ◽

Northern Eurasia ◽

Large Ensemble ◽

The Impact

AbstractThe ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.

Download Full-text

Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method

Journal of Climate ◽

10.1175/2011jcli3966.1 ◽

2011 ◽

Vol 24 (20) ◽

pp. 5275-5291 ◽

Cited By ~ 35

Author(s):

Bettina C. Lackner ◽

Andrea K. Steiner ◽

Gabriele C. Hegerl ◽

Gottfried Kirchengast

Keyword(s):

Climate Change ◽

Change Detection ◽

Confidence Level ◽

Geopotential Height ◽

Radio Occultation ◽

Lower Stratosphere ◽

Quality Data ◽

Climate Change Signal ◽

High Quality ◽

Climate Change Detection

Abstract The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

Download Full-text

On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa

Climate Dynamics ◽

10.1007/s00382-017-3547-7 ◽

2017 ◽

Vol 49 (11-12) ◽

pp. 3813-3838 ◽

Cited By ~ 24

Author(s):

Thierry C. Fotso-Nguemo ◽

Derbetini A. Vondou ◽

Wilfried M. Pokam ◽

Zéphirin Yepdo Djomou ◽

Ismaïla Diallo ◽

...

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Central Africa ◽

Added Value ◽

Climate Change Signal

Download Full-text

Evaluating quantile-based bias adjustment methods for climate change scenarios

10.5194/hess-2021-498 ◽

2021 ◽

Author(s):

Fabian Lehner ◽

Imran Nadeem ◽

Herbert Formayer

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Meteorological Data ◽

Climate Change Signal ◽

Historical Period ◽

Climate Change Scenarios ◽

Statistical Bias ◽

Bias Adjustment ◽

Direct Model

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

Download Full-text

Review of current approaches to spatially explicit urban vulnerability assessments: hazard complexity, data sources, and cartographic representations

GeoScape ◽

10.2478/geosc-2020-0005 ◽

2020 ◽

Vol 14 (1) ◽

pp. 47-61

Author(s):

Pavel Raška ◽

Martin Dolejš ◽

Jan Pacina ◽

Jan Popelka ◽

Jan Píša ◽

...

Keyword(s):

Climate Change ◽

Decision Support ◽

Data Sources ◽

Small Scale ◽

Spatially Explicit ◽

Decision Support Tools ◽

Multiple Hazards ◽

Vulnerability Assessments ◽

Support Tools ◽

Combined Data

AbstractSocio-ecological hazards are processes that − depending on the vulnerability of societal systems − may have profound adverse impacts. For this reason, the current discourse in disaster risk reduction (DRR) has been experiencing a shift toward a vulnerability-led paradigm, raising new questions about how to address (i) the complexity of vulnerabilities to multiple hazards, (ii) their cultural, dynamic, and subjective character, and (iii) the effectiveness and legitimacy of vulnerability assessments as decision-support tools. In this paper, we present a review of 707 vulnerability studies (derived from the Clarivate WoS database; 1988−2018) with a particular focus on urban settings and spatially explicit assessments in order to evaluate current efforts to meet the aforementioned issues. The reviewed studies assessed vulnerabilities to 35 hazard types that were predominantly (n=603, 85%) analysed as single hazards (mostly seismic, flood, and groundwater contamination hazards, as well as climate change), whereas only 15% (n=104) of studies focused on multiple hazards (mostly atmospheric hazards). Within the spatially explicit vulnerability studies, almost 60% used data collected by the study itself (mostly seismic hazards), while statistical and combined data were both employed in 20% of cases (mostly floods, climate change, and social and political hazards). Statistical data were found to have only limited transferability, often being generalised to be applicable in small-scale studies, while reducing the role of cultural and contextual factors. Field research data provided high-resolution information, but their acquisition is time-consuming, and therefore fixed at a local scale and single temporal stage. Underlying hazard types and suitable data sources resulting in other differences found a preference towards the specific coverage and resolution of vulnerability maps that appeared in 44% of all reviewed studies. Altogether, the differences we found indicated a division of spatially explicit vulnerability research in two major directions: (i) geological and geomorphological studies focusing on physical vulnerability, using their own data surveys at a detailed scale and lacking links to other hazards, and (ii) other studies (mostly atmospheric hazards and socialpolitical hazards) focusing on social or combined vulnerabilities, using primarily statistical or combined data at a municipal, regional, and country scale with occasional efforts to integrate multiple hazards. Finally, although cartographic representations have become a frequent component of vulnerability studies, our review found only vague rationalisations for the presentation of maps, and a lack of guidelines for the interpretation of uncertainties and the use of maps as decision-support tools.

Download Full-text