Impacts of Urban Albedo Increase on Local Air Temperature at Daily–Annual Time Scales: Model Results and Synthesis of Previous Work

2010 ◽  
Vol 49 (8) ◽  
pp. 1634-1648 ◽  
Author(s):  
E. Scott Krayenhoff ◽  
James A. Voogt

Abstract The authors combine urban and soil–vegetation surface parameterization schemes with one-dimensional (1D) boundary layer mixing and radiation parameterizations to estimate the maximum impact of increased surface albedo on urban air temperatures. The combined model is evaluated with measurements from an urban neighborhood in Basel, Switzerland, and the importance of surface–atmosphere model coupling is demonstrated. Impacts of extensive albedo increases in two Chicago, Illinois, neighborhoods are modeled. Clear-sky summertime reductions of diurnal maximum air temperature for the residential neighborhood (λp = 0.33) are −1.1°, −1.5°, and −3.6°C for uniform roof albedo increases of 0.19, 0.26, and 0.59, respectively; reductions are about 40% larger for the downtown core (λp = 0.53). Realistic impacts will be smaller because the 1D modeling approach ignores advection; a lake-breeze scenario is modeled and temperature reductions decline by 80%. Assuming no advection, the analysis is extended to seasonal and annual time scales in the residential neighborhood. Yearly average temperature decreases for a 0.59 roof albedo increase are about −1°C, with summer (winter) reductions about 60% larger (smaller). Annual cooling degree-day decreases are approximately offset by heating degree-day increases and the frequency of very hot days is reduced. Despite the variability of modeling approaches and scenarios in the literature, a consistent range of air temperature sensitivity to albedo is emerging; a 0.10 average increase in neighborhood albedo (a 0.40 roof albedo increase for λp = 0.25) generates a diurnal maximum air temperature reduction of approximately 0.5°C for “ideal” conditions, that is, a typical clear-sky midlatitude summer day.

2021 ◽  
Author(s):  
Achim Drebs ◽  
Tim Sinsel ◽  
Kirsti Jylhä

<p>In our research we describe the micro-climatological influences of two heat-waves around and the air temperature development in a certain old people’s home in Helsinki, Finland. The stand-alone six-storey concrete building was erected in the late 1970’s and represents the prevailing construction type of this area. The building is located on a slightly southwards declining slope.</p><p>The first simulation used real meteorological forcing-data from the heat-wave event in summer 2018, which lasted from July, 13<sup>th</sup> until August, 5<sup>th</sup>. In this period the daily maximum air temperature reached almost every day 25 °C and more, sometimes even more than 30 °C. All air temperature, wind, humidity, and solar radiation (cloudiness) measurements were conducted at a near-by synoptical weather station.</p><p>The second simulation used fourteen-day constructed meteorological forcing-data, based on a clear-sky, slowly increasing air temperature, higher than normal humidity, and low wind conditions assumption starting on July, 13<sup>th</sup> (day 194 of the year).</p><p>We used the holistic ENVI-met simulation soft-ware to simulate the physical environment around the old people’s home and especially the energy fluxes inside the concrete walls to explain the needs for cooling demands.</p><p>The research is part of the HEATCLIM-project financed by the Academy of Finland Science Program CLIHE (2020-2023).</p>


Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 68
Author(s):  
Arkadiusz M. Tomczyk ◽  
Ewa Bednorz ◽  
Katarzyna Szyga-Pluta

The primary objective of the paper was to characterize the climatic conditions in the winter season in Poland in the years 1966/67–2019/20. The study was based on daily values of minimum (Tmin) and maximum air temperature (Tmax), and daily values of snow cover depth. The study showed an increase in both Tmin and Tmax in winter. The most intensive changes were recorded in north-eastern and northern regions. The coldest winters were recorded in the first half of the analyzed multiannual period, exceptionally cold being winters 1969/70 and 1984/85. The warmest winters occurred in the second half of the analyzed period and among seasons with the highest mean Tmax, particularly winters 2019/20 and 1989/90 stood out. In the study period, a decrease in snow cover depth statistically significant in the majority of stations in Poland was determined, as well as its variability both within the winter season and multiannual.


2021 ◽  
pp. 29-39
Author(s):  
A. A. Poliukhov ◽  
◽  
D. V. Blinov ◽  
◽  

Aerosol effects on the forecast of surface temperature, as well as temperature at the levels of 850 and 500 hPa over Europe and the European part of Russia are studied using various aerosol climatologies: Tanre, Tegen, and MACv2. The numerical experiments with the COSMO-Ru model are performed for the central months of the seasons (January, April, July, and October) in 2017. It is found that a change in the simulated surface air temperature over land can reach 1C when using Tegen and MACv2 data as compared to Tanre. At 850 and 500 hPa levels, the changes do not exceed 0.4C. At the same time, it is shown that a decrease in the root-mean-square error of 2-m air temperature forecast at individual stations reaches 0.5C when using Tegen and MACv2 data and 1C for clear-sky conditions in Moscow.


2020 ◽  
pp. 1319-1327
Author(s):  
Osmar Bruneslau Scremin ◽  
José Antonio Gonzalez da Silva ◽  
Ivan Ricardo Carvalho ◽  
Ângela Teresinha Woschinski De Mamann ◽  
Odenis Alessi ◽  
...  

The fuzzy logic is an efficient tool for simulation and validation of new technologies in agriculture. The objective of the study is to adapt the fuzzy logic model for simulation of biomass and oat grain yield by nitrogen involving the nonlinearity of the maximum air temperature in the conditions of use of the biopolymer hydrogel, considering high succession systems and low release of residual N. The study was conducted in 2014 and 2015, in a randomized block design with four replicates in a 5 x 5 factorial. Five hydrogel doses (0, 30, 60, 90 and 120 kg ha-1) were added in the groove next to the seed; and 5 doses of N-fertilizer (0, 30, 60, 90 and 120 kg ha-1) applied at the fourth expanded leaf stage, respectively. The cultivar was URS Corona. The pertinence functions and the linguistic values established in the input and output variables to simulate the biomass yield and oat grains in the succession systems are adequate observed productivity. The fuzzy model makes it possible to estimate the biomass and oat grains productivity efficiently under the conditions of use of the hydrogel as a function of the nitrogen doses and maximum air temperature, adding to the existing models of simulation.


2019 ◽  
Author(s):  
Ari Sugiarto ◽  
Hanifa Marisa ◽  
Sarno

Abstract Global warming is one of biggest problems faced in the 21st century. One of the impacts of global warming is that it can affect the transpiration rate of plants that °Ccur. This study purpose to see how much increase in air temperature that occurred in the region of South Sumatra Province and to know the effect of increase in ari temperature in the region of South Sumatra Province on transpiration rate of Lansium domesticum Corr. This study used a complete randomized design with 9 treatments (22.9 °C, 23.6 °C, 24.6 °C, 26.3 °C, 27 °C, 27.8 °C, 31.7 °C, 32.5 °C, and 32.9 °C) and 3 replications. Air temperature data as secondary data obtained from the Meteorology, Climatology and Geophysics Agency (MCGA) Palembang Climatology Station in South Sumatra Province. The measurement of transpiration rate is done by modified potometer method with additional glass box. The data obtained are presented in the form of tables and graphs. Transpiration rate (mm3/g plant/hour) at temperture 22.9 °C = 4.37, 23.6 °C = 7.03, 24.6 °C = 8.03, 26.3 °C = 10.11, 27 °C = 13.13, 27.8 °C = 17.87, 31.7 °C = 23.21, 32.5 °C= 25.45 and 32.9 °C= 27.24. At the minimum air temperature in the region of South Sumatra Province there is increase in air temperature of 1.5 °C, average daily air temperature increase 1.3 °C and maximum air temperature increase 1.2 °C.


2015 ◽  
Vol 95 (4) ◽  
pp. 67-76
Author(s):  
Stanimir Zivanovic ◽  
Milena Gocic ◽  
Radomir Ivanovic ◽  
Natasa Martic-Bursac

Fires in nature are caused by moisture content in the burning material, which is dependent on the values of the climatic elements. The occurrence of these fires in Serbia is becoming more common, depending on the intensity and duration have a major impact on the state of vegetation. The aim of this study was to determine the association between changes in air temperature and the dynamics of the appearance of forest fires. To study the association of these properties were used Pearson correlation coefficients. The analysis is based on meteorological data obtained from meteorological station in Negotin for the period 1991-2010. Research has found that the annual number of fires, correlating with an average annual air temperature (p = 0.317, ? = 0.21). Also, it was found that the annual number of fires positive, medium intensity, correlate with the absolute maximum air temperature (p = 0.578, ? = 0.26), but not statistically significant (p> 0.05).


2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.


Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Ting Peng ◽  
Xiefei Zhi ◽  
Yan Ji ◽  
Luying Ji ◽  
Ye Tian

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.


Author(s):  
Xuelei Zhang ◽  
Weihua Xiao ◽  
Yicheng Wang ◽  
Yan Wang ◽  
Miaoye Kang ◽  
...  

Abstract This paper focuses on determining the spatial and temporal characteristics of the sensitivity coefficients (SCs) between potential evapotranspiration (ET0) and key climatic factors across the Shiyang River Basin (SYRB) from 1981 to 2015. Penman–Monteith equation and a sensitivity analysis were used to calculate ET0 and the SCs for key climatic factors. Sen's slope was used to analyze the observed series. According to the results, the sensitivity significances were in the order of relative humidity (RH) > net solar radiation (NSR) > wind speed (WS) > maximum air temperature (Tmax) > minimum air temperature (Tmin). The SCs for the RH and NSR were larger in the upper mountainous region, while the other three coefficients were larger in the middle and lower reaches. All five climatic factors for the ET0 SCs showed increasing trends in the mountainous region, and the Tmax, WS and RH SCs increased in the middle and lower reaches. Over the past 35 years, the change in ET0 was dominated by the air temperature (T), RH and NSR, and the increase in ET0 during the studied period was mainly due to the increases in T and NSR.


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