URBA6006 TsangNokSze 3035776660
Evaluation of Climate Model – Bias and Uncertainty in Climate Prediction
AcademicPaper–ClimateModel
PaperTitle Model
1 Quantitativeurbanclimatemappingbasedonageographical GIS-basedsimulation
database:AsimulationapproachusingHongKongasacase approach–MeansofSVF
study(Chen&Ng,2011) andFADsimulation
2 Applyingurbanclimatemodelinpredictionmode–evaluation MUKLIMO_3
ofMUKLIMO_3modelperformanceforAustriancitiesbased
onthesummerperiodof2019(Hollósietal.,2021)
3 Reanalysis-drivenclimatesimulationoverCORDEXNorth CandianRegionalClimate
AmericadomainusingtheCanadianRegionalClimateModel, Model
version5:modelperformanceevaluation(Martynovetal.,
2013)
4 Evaluationofextremeclimateeventsusingaregionalclimate RegionalClimateModel
modelforChina(Ji&Kang,2014) Version4.0
5 ExtremeclimateeventsinChina:IPCC-AR4modelevaluation RegionalClimateModel–
andprojection(Jiangetal.,2011) IPCCAR4
6 Afutureclimatescenarioofregionalchangesinextreme PRECIS,aregionalclimate
climateeventsoverChinausingthePRECISclimatemodel modelsystem
(Zhangetal.,2006)
7 ClimatechangeinChinainthe21stcenturyassimulatedbya RegionalClimateModel
high-resolutionregionalclimatemodel(Gaoetal.,2012) version3(RegCM3)
8 AregionalclimatemodeldownscalingprojectionofChina RegionalClimateModel
futureclimatechange(Liu,Gao&Liang,2012) version3(RegCM3)
9 ChangesinExtremeClimateEventsinChinaUnder1.5°C–4 RegionalClimateModel
°CGlobalWarmingTargets:ProjectionsUsinganEnsembleof (RgCM4)
RegionalClimateModelSimulations(Wuetal.,2020)
10 ClimateChangeoverChinainthe21stCenturyas RegionalClimateModel
SimulatedbyBCC_CSM1.**RegCM4.0(Gao,Wang&Giorgi, (RgCM4)
2013)
Introduction
The climate model is an extension of weather forecasting, it usually predicts how average conditions
will change in a region over the coming decades (Harper, 2018). To understand how to evaluate a
climate model, we should understand the components of a climate system. A Climate system is a
systemcombiningtheatmosphere,ocean,cryosphereandbiota,therefore,therearelotsofparameters
thatwillaffecttheclimatesituationofaregion.
The climate model is usually used by researchers to understand complex earth systems. The model
inputs will be the past climate data which acts as a starting point for typical climate systems analysis
and a model can be created and used to predict the future climatic situation as the model output.
Therefore, the more we learn from the past and present climatic situation, the more accuracy of the
modeltopredictthefutureclimaticsituation.
Model accuracy and precision depended on the following three major parts, includingInput, which is
related to the data quality and quantity; model which depended on the quality and quantity of
parameters,temporalandspatialextentsettings;andoutput,whichisabouttheaccuracyandprecision
oftheforecastingofthemodel.
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Evaluation
A) Complexityofmodel
Problemofparameters
There are increasing statistical methods of multimode climate projections, the complexity of the
model in analyzing different parameters also hence to enhance to predict different possibilities of the
futureclimaticsituation. However,mostoftheresearchersmentionedinthispaperareonlyinterested
in ranking the importance of the different parameters in affecting and controlling the climate system.
They will try to do some correlation between the parameters and the climate result to find which
parameters should be included in the climate model for prediction and analysis. However, what we
need to focus on is how these models predict the changes in the climate of the region, their ability to
predict the accurate trends of the climatic situation. It is important to note the complexity of the
climatemodelisnotinalinearrelationshipwithitsaccuracyinpredictingfuturetrends.
B) UncertaintyandBiasofthemodel
The uncertainty of the model causing overestimation and underestimation of the model in predicting
thetemperatureandprecipitation.
The issue of uncertainty and bias are the core parts of the climate change prediction problem. Due to
the complexity of these issues on both concept and speciality, uncertainty and bias will remain an
inevitableissuesinthedebateofclimatechange.
Theproblemoftopography
As indicated by much research on climate models based in China, the problem of topography is the
major limitation for the collection of data in the first stage. China is known as a country with
complicated topography, including mountains, basins, plateaus, hills, and plains. It is important to
note that complicated topography largely affects the climate models stability (Mesinger & Veljovic,
2020), and this topography characteristic has been reviewed by Martynov et al. (2013), Jiang et al
(2011)andZhangetal(2006)asthebarriersindatacollection.
For example, as stated in research of Martynov et al (2013), the horizontal resolution in the climate
simulation is insufficient for such a complex topographical situation, while the vertical interpolation
of the pressure gradient simulation is also affected by the complex topographical factors. Similar to
theresults as statedintheresearchof Jianget al(2011),the complexityofthe topology inChina also
affect the accuracy of the model in predicting future precipitation, especially for the case of
topography-driven precipitation, the related data is not well measured and recorded by the coarse
resolution model. Mountainous regions of China also induced bias issues. Some weather stations
locatedinthevalleyorlowelevationregionsmayalsoresultinthecoldbiasoftheclimatemodelling
results. As reviewed in the regional climate model in research of Zhang et al (2006), the operation of
complex topography in China with the strong monsoon system causing a large spatial variability in
thepredictionaccuracyoftheclimatesystem.
Theproblemofhumidity
Both humidity and temperature are the major components in the climate model while humidity has
long struggled in the climate models in whether it has been adequately represented the cloud systems
to tropospheric humidity in the calculation of the climate system. In the research done by Ji & Kang
(2014), the factor of humidity in the formulation of climate systems becomes the greatest uncertainty
inclimatemodelprediction.TheclimatemodelstatedinJi&Kang(2014)researchalsoindicatedthe
relative humidity prediction appears to be much less credible and show a large variety of model
predictionskills.
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It is necessary to include a comprehensive analysis of the dynamic cloud processes so to evaluate the
humidityeffect inthe climate model. Moreover,humidityis highlyvariable over small scales of time
andspace,whichisahugeuncertaintyfortheregionalclimatemodel,thiswillleadtoalargerangeof
potential results in the future, directly affect the forecasting ability of the model. (Maslin & Austin,
2012).
Theavailabilityofobservationaldata
Climate observations are used as a baseline for accessing climate changes. As revealed in some
researches, complicated topography that falls within a large range of elevation largely affect data
quality and quantities of climate data collected. For instance, the temperature and humidity related
data are hardly collected. For example, for the Hollósi et al (2021) research on applying climate
models for Austrian cities, the problem of uneven distribution of weather stations is found. In other
cities of Austria, because of the limited number andsparsely placeddata collection stations, there are
muchlessobservationaldataofsome ruralregions.Evenifthecitieshavearelativelyhighamount of
weather stations, due to the building geometry differences between rural and urban cities
environmentalsetting,somepatternssuchasheatloadisnotproperlyinvestigatedandmonitored.
Therefore, the quality and quantities of the observational data are not stable and reliable for some
climate modes, resulting in large uncertainties and difficulties when analysing the climatic difference
betweenurbanandruralareas.
C) Theforecastingabilityofthemodel
The limited forecasting ability of the climate model is not inevitable. It is so hard to predict climate
changes, which highly depends on the data quality measured and captured by the measurement
stationsorequipment(Maslin& Austin,2012).Also,ouratmosphericstructureis socomplicatedand
the climatic situation is affected by many external factors that cannot be analyzed and found out by
onesingleclimaticmodel(Herrington,2019).
Theproblemofusingpastclimaticdatainpredictingextremeweather
It is important to note that climate has changed so extremely and intensely that the frequency of past
extreme eventsisnolongerareliablepredictor, especiallyforthehuman-inducedwarminghasonthe
extremeevents.Hence,theuseoftemporallylaggedperiodsofextremeeventsprobablywillprobably
underestimatethehistoricalimpacts,andalsounderratetherisksoftheoccurrenceofextremeweather.
As stated by Foley (2010), the technique that using historical observation data to calibrate future
model projections is not precise enough when the model is trying to simulate and validate a state of
the system that has not been experienced before. This is an inevitable barrier for the model
computationsofthenaturalsystems.
Researches done by Ji & Kang (2014), Jiang et al (2011) and Gao, Wang & Giorgi (2013) tries to
predict extreme weather by using the historical data at different ranges, basically using the range of
the temperature as the observational data as the input of the model. Sometimes the problem of
complicated topography of China will also induce large biases in the collection of climatic data,
includes the daily mean temperature and the records minimum and maximum temperature. As
mentioned by Sillmann et. al., (2017), predicting extreme weather needed to depend on the presence
of large scale drivers, which should be the major contributors to the existence of extreme weather.
Therefore, instead of using the separate dynamic and physical processes in the predictive model to
predict climate changes as stated in research Ji & Kang (2014), Jiang et al (2011) and Gao, Wang &
Giorgi (2013), the researches should focus on the interrelationship between the processes, a better
understandingof the processes canallowus torealize the underlyingdrivers of theresults of extreme
weather.
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OverestimationandUnderestimation
The climate models overestimated the interannual variability of temperature. As indicated in the Ji &
Kang(2014)research,thenetworkofprecipitationpatternsthatareprocessedfromstationsinthearid
areas may underestimate the precipitation over the northern topography of China. While the Jiang et
al (2011) research indicated the regional climate model tends to overestimate the precipitation
situationinthenorthernandwesternpartsofChinawhereintenseprecipitationisrarelyfound.Onthe
other hand, the climate model also underestimatedthe precipitation that will exist in the southern and
northeastern parts of China in the future. A similar result was also found in the Zhang et al (2006)
research,whichindicatedthattheclimatemodelunderestimatedtheexistenceofextremeprecipitation
eventsinthesouthernpartofChina.
For the climate model researches done in Hong Kong (Chen & Ng, 2011), only building geometry is
takingintoconsiderationinclimatesimulation,bothtopographyandvegetationcoverarenotincluded,
indicated that the results may overestimate the real temperature for the location located in higher
elevationwithlargevegetationcover.
LimitationoftheRegionalSimulationsinRegionalClimateModel
Mostoftheresearchesindicatedinthispaperfocusontheregionalclimatemodel,whichisthehigher
resolution model compared to the global climate model. Therefore, with a finer resolution of the
regional climate model, scientists can have a higher ability in resolving mesoscale phenomena that
contributing to heavy precipitation (Jones, Murphy & Noguer, 1995). However, as the regional
climate model onlycover certainparts ofthecontinental, thelateral boundaryconditionis requiredin
the model simulation. Therefore the accuracy of regional simulations is highly dependent on the
boundaryconditions of the observations. When the regional climate model is affected by some cross-
boundary external forcings, uncertainties must have easily existed when the climate model trying to
forecastorprojectthefutureclimateinboundaryconditions.(CCSP,2008)
Conclusion
Formulation and using a climate model to analyze the climate data and making the prediction is
becoming a new trend for scientists and researchers to enhance our understandings of the earth we
lived on. With the increased complexity of the climate model, more and more factors are putting into
considerations when we trying to predict the climate situation. However, despite the climate model
are more sophisticated in today’s society, biases and uncertainties still existed, but we should also
needtounderstandthat there is noperfect modelwith nobias anduncertainty. As longas the climate
modelisabletoensureanddecidethesensitivityoftheactualclimatesystemtosmallexternaldrivers,
theweightof scientificevidence isalreadyenoughtogive us the informationandmake anacceptable
predictionoftheclimaticsituationofourworld.
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