Olivia Kellner, PhD. Lead Research Scientist, Climate Impact Company
China, like the United States, is home to a variety of topographic features, land use types (i.e. urban, rural, forest, grassland, water), and large population centers. While both countries are in the northern hemisphere and share the same seasons, China is slightly farther south than the United States from the geographic North Pole. This results in different weather patterns throughout the year for China, whose summer climate and weather are largely influenced by the Asian Monsoon season. Tibet is an autonomous region in China, and its physiographic characteristics make it the primary driver of the Asian Monsoon Season. While the Tibetan Plateau is the primary driver of the Asian Monsoon season, human modification of the climate system in eastern China can influence the evolution of weather as it responds to the summer Asian Monsoon, and the evolution of weather in Tibet.
It has been well established over the last several decades that human modification of the natural climate system can influence the temporal and spatial evolution of weather patterns up to the synoptic scale. This does not dismiss the natural climate drivers that also influence weather and climate across China and Tibet (e.g. sea surface temperatures anomalies such as ENSO and the PDO, the Eurasian snow pack, and the uneven heating of the land surface of the continent), but highlights how forecast models are not enough to suffice for an accurate forecast.
Numerical forecast models continue to lack in forecast accuracy for SST anomaly forecasting, snowfall/snow pack and radiation dynamics, and surface heat flux/planetary boundary dynamics to accurately capture these dynamics in forecast models. Thus, it is important that a forecaster be aware of land-surface interactions and human modification of the climate system to develop the best forecast possible. The scope of this research summary is to focus on how the change to China’s eastern landscape (e.g. urbanization/land-use change and increased pollution) has impacted weather patterns for Tibet. It also discusses forecast accuracy limitations due to a lack of observational weather data to verify and improve the newest forecast methodologies.
To best understand the drivers of China’s and Tibet’s climate, it is easiest to begin looking at the topographic features that define the country (China) and the region (Tibet) (Figure 1 (A)). China’s topography is predominantly characterized by the Tibetan Plateau in the west (Figure 1(B)), which gives way to mountainous regions to the east, finally to more valleys and gently sloping land near where the Yangtze River Valley enters the Yellow Sea. The Tibetan Plateau is a region characterized by some of the highest elevations in the world, and is a predominant driver of the Asian Monsoon season. As the slopes of the plateau warm during the northern hemisphere summer months from increased solar radiation and stronger angles of incident solar radiation, a strong low pressure system develops over the Tibetan Plateau which draws warm, moist air northward from the South China Sea, the Bay of Bengal, and the Pacific and Indiana Oceans into China.
Figure 1: (A) Map showing the location of Tibet. (B) Topographic map of China showing it is a predominantly mountainous country. The darker tan/brown colors indicate higher elevations and the greens indicate the lowest elevations (C) The predominant wind patterns of the East-Asian Monsoon that develops during the northern hemisphere winter (Dec to Mar), when the land over much of continental Asia is colder than the ocean. This causes sinking cold air over land to blow towards the ocean. (D) The predominant wind patterns that develop during northern hemisphere summer ( Jun to Sep), when the Tibetan Plateau and Asian landmass heat up faster than the oceans due to the increased solar radiation as the sun is north of the equator.
It is during the northern hemisphere summer months of June to September when the East Asian Monsoon winds reverse and flow from the oceans to the land that Tibet is most likely to see weather modification from the urban centers and land-use change patterns across eastern China. From this point onward, the focus will be on the summer monsoon season, as this is when winds shift and transport air masses from over the highly populated, urbanized regions. Figure 2 shows the growth of urban areas and land use changes China has experienced through time.
Figure 2: As copied from Miao et al., 2013, this figure shows the spatial distribution of urban land use over the years between two datasets. The urbanization of eastern China has grown significantly over the last approximately 200 years. The most recent urbanization expansion occurred during the 1990’s to early 2000’s.
Observations Changes to China’s Weather and Tibet’s Weather
Precipitation: Numerous studies show a shift in precipitation patterns across China and Tibet. The consensus is that natural climate variability, along with anthropogenic modification of the atmosphere through pollution/increased green house gases (GHG) and land use land cover change (LULCC) have resulted in shifts to observed precipitation across China and Tibet. China has seen a decrease in light rain events and an increase and heavy rain events resulting in increasing drought periods. However, the eastern portion of the Tibetan Plateau has seen an increase in wet conditions during the last 40 years while the northeast Tibetan Plateau has experienced less rainfall. Shifts in precipitation are also noted/recognized to vary significantly between regions. For Tibet specifically, the amount of precipitation it experiences is highly variable due to the topography. Summer precipitation also accounts for 60% of the total annual precipitation in Tibet making it an important forecast parameter for the sustainment of life in the area.
Temperature: All of China has seen an increase in average temperature over the last several decades. This has resulted in a reduction in freeze events and longer growing seasons, similar to the United States. On the Tibetan Plateau, temperatures have warmed the most during the winter season. This results in a shorter snowfall season, reduced snow pack, and less soil moisture in the spring time available to the hydro-climate cycle.
Winds: Near-surface winds in China (including Tibet) have actually subsided (lessened) over the last several decades (Figure 3). Near surface winds are those not significantly impacted by the upper air currents (e.g. planetary waves and jet streams) but those winds that are near or within the planetary boundary layer (PBL). These winds are impacted by surface radiation (sensible and latent heat fluxes), surface soil moisture and vegetation, PBL moisture profiles, and roughness of the surface (determined by height of vegetation, trees, or buildings). China’s intense urbanization in the eastern portion of the country largely impacts near-surface winds that blow in from the oceans during the summer East Asian Monsoon. The south, southeast, and easterly winds (recall Figure 1D) blow through the highly urbanized regions before reaching the Tibetan Plateau. The urban cities disrupt the surface flow, add aerosols and pollutants, and mix hotter, drier air (urbanized areas have a much greater sensible heat flux than latent heat flux) into the air mass before it moves farther inland.
Figure 3: As adapted from Guo, et al., 2010, surface observations of wind speeds show a decline for urban and rural areas across China (A), and for all seasons across China with spring experiencing the greatest decrease in speeds compared to the other seasons (B).
During summer East Asian Monsoon season, winds push the more polluted, stagnant air masses toward the Tibetan Plateau. The winds reach the plateau and tend to stagnate along the eastern slopes of the plateau. This results in a series of physical atmosphere processes that impact the weather observed in Tibet, most specially eastern Tibet, and that current forecast models fail to adequately capture.
Forecast models are a series of complex equations, known as parameterization schemes, which forecast how the weather evolves through time. Each parameterization scheme should be considered as a separate forecast model, which communicates to the other parameterization schemes/forecast models to determine a final forecast. The following are parameterization schemes common to all operational forecast models:
Boundary layer physics
Surface layer physics
Planetary Boundary layer physics
Additional input data/parameters that influence the forecast:
Land-use data file (there are a variety, and most are dated)
Soil texture (again, dated information)
Roughness length (determined by land use type)
Leaf Area Index
Availability of an atmospheric Chemistry/Aerosol Model and the data needed for it
The primary issues with operational forecast models is that parameterization schemes 1) take years to validate; 2) are not compatible with all forecast models; and 3) are limited to model resolution (i.e. if model resolution is large, then it is not suitable to use some of the smaller models such as microphysics and cloud physics). This can impact a forecast.
Validation of parameterization schemes is highly important, as it demonstrates the model’s ability to accurately forecast. Validation is done by comparing a forecast to observed data. Tibet is a region of vast topography and has limited weather observation platforms. This makes verification of model forecasts difficult for this area.
Recalling that the focus of this paper is human modification of the climate system, the accuracy and timeliness of land-use data file is highly important since we are discussing the impacts of urbanization in Eastern China on weather in Tibet. Land-use data files are developed from satellite data. Updated satellite data at resolutions needed for forecast models is typically only released every 10 years due to data processing times, and validation of the data. For example, the GFS model is currently using the Noah-LSM, which allows a choice between two land use data files: 1) MODIS (combination of MODIS-Terra and Aqua data) and 2) USGS 30-second global 24-category vegetation (land-use) map. Both of these options are somewhat dated, using imagery from the early to mid 2000s. They also differ slightly based on the remote sensing processing algorithms used to interpolate the gridded datasets. This results in 1) dated land-use data and 2) different forecasts.
Lastly, the inclusion/application of an atmospheric chemistry model and aerosol impacts to radiation and precipitation generation is highly limited in global operational models. Many parameterization schemes/models for chemistry and aerosol impacts that capture the detail needed to predict rainfall are only available for research-based forecast models. Global forecast models use very simple approximations and average seasonal value of GHGs and aerosol data. This results in a less accurate forecast.
In regards to China’s impact on Tibet’s weather and the ability of forecast models accurately forecast, the most important factors to consider are:
1) Validation of forecast data in the Tibetan Plateau region is highly limited, limiting the accuracy of the model
2) Land-use data used in forecast models is aged. This does not capture current land-use patterns in highly urbanized areas that impact surface radiation budgets (urban areas have higher a sensible heat flux which increases air temperatures. The reduced latent heat flux results in drier air.). City-scapes also disrupt airflow.
3) Limited application of atmospheric chemistry models in global forecast systems result in models failing to capture the impact of aerosol and pollution transport across China during the summer East Asian Monsoon season.
The reduction of near-surface wind speeds across China has resulted in air stagnation events, especially just east of the Tibetan Plateau (Figure 4). Particulate matter, aerosols, and other pollutants act as cloud condensation nuclei that have been found to increase precipitation rates downwind of where the airmass ascent occurs. Orographic ascent of the more stagnant, polluted airmasses up the eastern slopes of the Tibetan Plateau is likely a cause of the increased precipitation observed across the Eastern Tibetan Plateau. The ability of operational models to capture such a phenomena is lacking. This demonstrates the need of forecasters to know the additional influencing factors (e.g. anthropogenic) of weather generation outside of regular atmospheric processes, and the need for continued support of weather and climate system research for improved forecast models.
Figure 4: As copied from Q. Huang, et al, 2017, stagnation of air masses east of the Tibetan Plateau is common through all seasons. Data is from the years 1985-2014.
Eastern China’s recent urbanization and land-use changes, coupled with increased pollution, over the last several decades has been a contributing factor (natural variability has been contributing as well) to the changes in temperatures, wind patterns, and precipitation across the country. The urbanization in eastern China has impact air mass characteristics that eventually reach Tibet, resulting in a change in weather patterns. In Tibet, the air pollution transported by the East Asian Monsoon has likely contributed to the increased rainfall observed in Eastern Tibet and drier conditions in other portions of Tibet.
Figure 5: As provided in Zhao et al., 2017, this is a map of the mean drought frequency in China’s provinces, municipalities and autonomous regions from 1982 to 2010. Shading represents drought frequency with red colors indicating a higher frequency and green colors a lower drought frequency. The magenta circles are representative of the mean drought frequency. Circled in red is eastern Tibet where drought frequency is lower and precipitation has increased over the last several decades. The increased pollution and particulate matter damning against the slopes of the plateau act as cloud condensation nuclei when the air mass is lifted up the slope. This results in heavier rain events in this region, with less rainfall falling over the rest of the plateau.
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