URBA6006 Climate Model

URBA6006 

  

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_CSM1.1-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.