Geothermal climate change observatory in south India 1: Borehole temperatures and inferred surface temperature histories

The coral reef ecosystem in Cu Lao Cham, Vietnam is part of the central zone of the Cu Lao Cham -Hoi An, a biosphere reserve and it is strictly protected. However, the impacts of natural disasters - tropical cyclones (TCs) go beyond human protection. The characteristic feature of TCs is strong winds and the consequences of strong winds are high waves. High waves caused by strong TCs (i.e. level 13 or more) cause decline in coral cover in the seas around Cu Lao Cham. Based on the relationship between sea surface temperature (SST) and the maximum potential intensity (MPI) of TCs, this research determines the number of strong TCs in Cu Lao Cham in the future. Using results from a regional climate change model, the risk is that the number of strong TCs in the period 2021-2060 under the RCP4.5 scenario, will be 3.7 times greater than in the period 1980-2019 and under the RCP 8.5 scenario it will be 5.2 times greater than in the period 1980-2019. We conclude that increases in SST in the context of climate change risks will increase the number and intensity of TCs and so the risk of their mechanical impact on coral reefs will be higher leading to degradation of this internationally important site.

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Comparison of two Centennial-scale Sea Surface Temperature Datasets in the Regional Climate Change Studies of the China Seas

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/81/1/012077 ◽

2017 ◽

Vol 81 ◽

pp. 012077 ◽

Cited By ~ 1

Author(s):

Wang Qingyuan ◽

Wang Yanan ◽

Liu Yiwei

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Regional Climate ◽

Regional Climate Change ◽

Sea Surface ◽

China Seas ◽

The China Seas

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Can an Earth System Model simulate better climate change at mid-Holocene than an AOGCM? A comparison study of MIROC-ESM and MIROC3

Climate of the Past ◽

10.5194/cp-9-1519-2013 ◽

2013 ◽

Vol 9 (4) ◽

pp. 1519-1542 ◽

Cited By ~ 4

Author(s):

R. Ohgaito ◽

T. Sueyoshi ◽

A. Abe-Ouchi ◽

T. Hajima ◽

S. Watanabe ◽

...

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Earth System Model ◽

Sea Surface ◽

System Model ◽

Earth System ◽

Intercomparison Project ◽

The Difference ◽

Precipitation Enhancement

Abstract. The importance of evaluating models through paleoclimate simulations is becoming more recognized in efforts to improve climate projection. To evaluate an integrated Earth System Model, MIROC-ESM, we performed simulations in time-slice experiments for the mid-Holocene (6000 yr before present, 6 ka) and preindustrial (1850 AD, 0 ka) periods under the protocol of the Coupled Model Intercomparison Project 5/Paleoclimate Modelling Intercomparison Project 3. We first give an overview of the simulated global climates by comparing with simulations using a previous version of the MIROC model (MIROC3), which is an atmosphere–ocean coupled general circulation model. We then comprehensively discuss various aspects of climate change with 6 ka forcing and how the differences in the models can affect the results. We also discuss the representation of the precipitation enhancement at 6 ka over northern Africa. The precipitation enhancement at 6 ka over northern Africa according to MIROC-ESM does not differ greatly from that obtained with MIROC3, which means that newly developed components such as dynamic vegetation and improvements in the atmospheric processes do not have significant impacts on the representation of the 6 ka monsoon change suggested by proxy records. Although there is no drastic difference between the African monsoon representations of the two models, there are small but significant differences in the precipitation enhancement over the Sahara in early summer, which can be related to the representation of the sea surface temperature rather than the vegetation coupling in MIROC-ESM. Because the oceanic parts of the two models are identical, the difference in the sea surface temperature change is ultimately attributed to the difference in the atmospheric and/or land modules, and possibly the difference in the representation of low-level clouds.

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Effects of Forest Changes on Summer Surface Temperature in Changbai Mountain, China

Forests ◽

10.3390/f12111551 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1551

Author(s):

Jiaqi Zhang ◽

Xiangjin Shen ◽

Yanji Wang ◽

Ming Jiang ◽

Xianguo Lu

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Land Surface ◽

Climate Models ◽

Regional Climate ◽

Changbai Mountain ◽

Vegetation Coverage ◽

Area Index ◽

Forest Coverage ◽

Cooling Effects

The area and vegetation coverage of forests in Changbai Mountain of China have changed significantly during the past decades. Understanding the effects of forests and forest coverage change on regional climate is important for predicting climate change in Changbai Mountain. Based on the satellite-derived land surface temperature (LST), albedo, evapotranspiration, leaf area index, and land-use data, this study analyzed the influences of forests and forest coverage changes on summer LST in Changbai Mountain. Results showed that the area and vegetation coverage of forests increased in Changbai Mountain from 2003 to 2017. Compared with open land, forests could decrease the summer daytime LST (LSTD) and nighttime LST (LSTN) by 1.10 °C and 0.07 °C, respectively. The increase in forest coverage could decrease the summer LSTD and LSTN by 0.66 °C and 0.04 °C, respectively. The forests and increasing forest coverage had cooling effects on summer temperature, mainly by decreasing daytime temperature in Changbai Mountain. The daytime cooling effect is mainly related to the increased latent heat flux caused by increasing evapotranspiration. Our results suggest that the effects of forest coverage change on climate should be considered in climate models for accurately simulating regional climate change in Changbai Mountain of China.

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Co-occurrence of extreme ozone and heat waves in two cities from Morocco

Satellite Oceanography and Meteorology ◽

10.18063/som.v3i3.733 ◽

2018 ◽

Vol 3 (3) ◽

Cited By ~ 4

Author(s):

Kenza KHOMSI 1,2 ◽

Houda NAJMI 2 ◽

Zineb SOUHAILI 1

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Extreme Events ◽

Global Climate Change ◽

Large Scale ◽

Heat Waves ◽

Global Climate ◽

Meteorological Factor ◽

Two Cities ◽

Change Context

Temperature is the first meteorological factor to be directly involved in leading ozone (O3) extreme events. Generally, upward temperatures increase the probability of having exceedance in ozone adopted thresholds. In the global climate change context more frequent and/or persistent heat waves and extreme ozone (O3) episodes are likely to occur during in coming decades and a key question is about the coincidence and co-occurrence of these extremes. In this paper, using 7 years of surface temperature and air quality observations over two cities from Morocco (Casablanca and Marrakech) and implementing a percentile thresholding approach, we show that the extremes in temperature and ozone (O3) cluster together in many cases and that the outbreak of ozone events generally match the first or second days of heat waves. This co-occurrence of extreme episodes is highly impacted by humidity and may be overlapping large-scale episodes.

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Estimates of climate system properties incorporating recent climate change

Advances in Statistical Climatology Meteorology and Oceanography ◽

10.5194/ascmo-4-19-2018 ◽

2018 ◽

Vol 4 (1/2) ◽

pp. 19-36 ◽

Cited By ~ 3

Author(s):

Alex G. Libardoni ◽

Chris E. Forest ◽

Andrei P. Sokolov ◽

Erwan Monier

Keyword(s):

Climate Change ◽

Surface Temperature ◽

21St Century ◽

Climate Sensitivity ◽

Temperature Rise ◽

Heat Content ◽

Climate System ◽

Model Parameters ◽

Aerosol Forcing ◽

Global Mean

Abstract. Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for the global mean, climate sensitivity, and ocean diffusivity.

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Global long-term land surface temperature for NOAA AVHRR: extension from 1981-2000 to 1981-2020

10.5194/egusphere-egu21-14373 ◽

2021 ◽

Author(s):

Jin Ma ◽

Ji Zhou

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Global Scale ◽

Mean Bias Error ◽

Bias Error ◽

Important Indicator ◽

Noaa Avhrr

As an important indicator of land-atmosphere energy interaction, land surface temperature (LST) plays an important role in the research of climate change, hydrology, and various land surface processes. Compared with traditional ground-based observation, satellite remote sensing provides the possibility to retrieve LST more efficiently over a global scale. Since the lack of global LST before, Ma et al., (2020) released a global 0.05 &#215;0.05&#160; long-term (1981-2000) LST based on NOAA-7/9/11/14 AVHRR. The dataset includes three layers: (1) instantaneous LST, a product generated based on an ensemble of several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST at 14:30 solar time; and (3) monthly averages of ODC LST. To meet the requirement of the long-term application, e.g. climate change, the period of the LST is extended from 1981-2000 to 1981-2020 in this study. The LST from 2001 to 2020 are retrieved from NOAA-16/18/19 AVHRR with the same algorithm for NOAA-7/8/11/14 AVHRR. The train and test results based on the simulation data from SeeBor and TIGR atmospheric profiles show that the accuracy of the RF-SWA method for the three sensors is consistent with the previous four sensors, i.e. the mean bias error and standard deviation less than 0.10 K and 1.10 K, respectively, under the assumption that the maximum emissivity and water vapor content uncertainties are 0.04 and 1.0 g/cm2, respectively. The preliminary validation against in-situ LST also shows a similar accuracy, indicating that the accuracy of LST from 1981 to 2020 are consistent with each other. In the generation code, the new LST has been improved in terms of land surface emissivity estimation, identification of cloud pixel, and the ODC method in order to generate a more reliable LST dataset. Up to now, the new version LST product (1981-2020) is under generating and will be released soon in support of the scientific research community.

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