Climate Science, models and projections: A perspective MCT PHASE-IV 2014 MODULE: ENVIRONMENT 14 th 20 th May 2014, Dehradun Saurabh Bhardwaj Associate Fellow Earth Science & Climate Change Division TERI
Factors influencing climate Incident solar radiation - variation with latitude Closeness to large water bodies - distribution of land & water Mountain barriers Altitude Ocean temperature and currents Land cover Atmospheric composition
Radiation is not evenly distributed over the surface of the earth. The highlatitudes have an energy deficit and the low latitudes has excess. But the low latitudes don t indefinitely get hotter and the high-latitudes don t get colder. Why? The atmosphere and ocean transfer energy from low to high latitudes
Atmospheric Circulation Warm tropics transfer heat to poles with the help of atmospheric circulation in the north-south direction Source: Univ of California San Diego through Hadley Cell, Ferrell cell and Polar cell.
Oceanic Circulation Sinking cool water, rising warm water and wind help to form global ocean Ocean circulation Surface currents in more systems. detail The Great Ocean Conveyor TERI-BCCR Climate Research School Courtesy: http://worldoceanreview.com Pic courtesy NASA, CSIRO
Interactions The non-linear interaction among the components leads to climate variability at a range of spatial and temporal scales Pic. NOAA
How do we quantify the response of the climate? The response of the climate system to this forcing agents is complicated by: feedbacks the non-linearity of many processes different response times of the different components to a given perturbation The only means available to calculate the response is by using numerical models of the climate system.
What is a Model? a simplified description, esp. a mathematical one, of a system or process, to assist calculations and predictions - Mac dictionary How do we define a Climate Model? A climate model is a mathematical representation of the physical processes that determine climate Why do we need Climate Models? To create an understanding of the climate processes. To create plausible-scenarios, reflecting the current state of scientific understanding. To plan for the future.
Observations Theory Models McGuffie, Warner K. (2011) and Henderson-Sellers, Numerical Weather A. and (2005) Climate A Climate Prediction. Modelling Cambridge Primer. University 3rd ed., Press. Wiley.
Components of Climate models Radiation input and absorption of solar radiation and emission of infrared radiation handled. Dynamics horizontal movements of energy around the globe (low to high lat.) and vertical movements (convection etc.) Surface processes inclusion of land/ocean/ice and the resultant change in albedo, emissivity and surface-atmosphere energy interactions. Resolution in both time and space the time step of the model and the horizontal and vertical scales resolved.
Components of a weather/climate model McGuffie, K. and Henderson-Sellers, A. (2005) A Climate Modelling Primer. 3rd ed., Wiley.
Modelling Climate Vertical exchange between layers of momentum, heat and moisture 60 N 15 W 3.75 2.5 Horizontal exchange between columns of momentum, heat and moisture Vertical exchange between layers of momentum, heat and salts by diffusion, convection and upwelling Vertical exchange between layers by diffusion and advection 11.25 E 47.5 N Orography, vegetation and surface characteristics included at surface on each grid box
Numerical Solution: Time steps and Grid boxes All the physical processes occurring in the climate system are resolved at individual grid and the coupling occurs at these grids. Source: NASA
Framework for a Model Source: MPI, Germany
Process of Model Simulation Generation of model source code Model Simulation Supply of Initial and boundary conditions Source: Goosse et al 2010
Development of climate models 2000 2005
The complexity of simulating the earth's climate system using global climate models has undergone a lot of changes from 1970s - till AR4.
Improvements in Grid resolution The evaluation of the Climate models has become an essential prerequisite to understand the Earth s climate system A Model Inter-comparison Project is an approach to model verification and they are part of community analysis and verification/activity. Intergovernmental Panel for Climate Change has started its MIP programs with Atmospheric Models in 1995 till today with CMIP (Coupled Ocean Atmospheric Models).
Computational Capabilities and Needs Improvements in computational capabilities have paved the developments of atmospheric simulation capabilities Source: NCAR Source: McGuffe, Henderson and sellers As an example, a 10-year global atmospheric simulation using a state-of-art GCM can require several tens of hours of supercomputer ( Flops time approx 10 9 floating operations per second (1 Giga
What can we expect to simulate? 1. Basic features of the general circulation of the atmosphere (e.g. Hadley cell, mid-latitude jets) 2. Climatology (based on at least 5-10 years) e.g. seasonal and monthly means. 3. Climate variability, e.g. behaviour of dominant modes of interannual variability such as ENSO, NAO. 4. Statistics of sub-seasonal variability e.g. monsoon active/break cycles, storm-track characteristics
What can we not expect to simulate? 1. The actual weather observed at individual locations, at specific times. 2. A 100 % correlation with observations due to inherent climate uncertainty. Hence, ensemble approach is utilized. 3. Individual weather events. But climatological statistics able to provide future frequency and magnitude of such events.
What do we look for in model predictions/projections? A major focus is on global mean temperature. However other, statistical, predictions are studied (we extract both means and variation from model runs) Rainfall: how is it distributed spatially and seasonally; does it come as more intense downpours; is it likely to rapidly re- evaporate due to higher temperatures? Changes in winter storms or tropical cyclones? Temperature: will there be more heat-waves (periods of several days that are too hot ) or other extremes?
Simulations using a Global Coupled Model: The simulations of a model should be comparable to the observations, this step is called as Validation of the model outputs Source: TERI (2011)
Typical data used to evaluate climate models Re-analyses of the global circulation (ERA40, NCEP) Synthesised climatologies e.g. precipitation Satellite observations In situ measurements
Need for Regional Climate Modeling Tool Most of AR4 coupled models even with high spatial resolution of 110km x 110km Most of AR4 coupled models even with high spatial resolution of 110km x 110km were unable to represent the mean monsoon pattern similar to observations. were unable to represent the mean monsoon pattern similar to observations.
Downscaling from GCMs Downscaling is a way to obtain higher spatial resolution output based on GCMs. Options include: Combine low-resolution monthly GCM output with high-resolution observations Use statistical downscaling Easier to apply Assumes fixed relationships across spatial scales Use regional climate models (RCMs) High resolution Capture more complexity Limited applications Computationally very demanding
( RCMs ) Regional Climate Models These are high resolution models that are nested within GCMs A common grid resolution is 50 km or lesser. RCMs are run with boundary conditions from GCMs They give much higher resolution output than GCMs Hence, much greater sensitivity to smaller scale factors such as mountains, lakes
Regional Modelling Product RCM is able to capture the major features but overestimates the rainfall in few regions. Source: TERI (2011)
The Scientific Method in order to be genuinely scientific, a statement must be susceptible to testing that could potentially show it to be false (Popper, 1934). In practice, contemporary scientists usually submit their findings to the scrutiny of their peers, so that the results can be checked through replication by other scientists. The insights and research results of individual scientists, even scientists of unquestioned genius, are thus confirmed or rejected in the peer-reviewed literature (IPCC AR-4, WG-I, Chapter 1) Thus the scientific method comprises: Empirical testing/refutation of hypotheses Peer review and publication Open debate about alternative hypotheses
Intergovernmental Panel on Climate Change (IPCC) IPCC was established in 1988 by WMO and UNEP and consists of about 190 Governments. Role of the IPCC The role of the IPCC is to assess on a comprehensive, objective, open and transparent basis the scientific, technical and socio-economic information relevant to understanding the scientific basis of risk of human-induced climate change, its potential impacts and options for adaptation and mitigation. Review by experts and governments is an essential part of the IPCC process. The Panel does not conduct new research, monitor climate-related data or recommend policies. It is open to all member countries of WMO and UNEP. IPCC is policy relevant, but not policy prescriptive!
The IPCC Process The IPCC is funded by the member governments as a UN body
Radiative forcing source: IPCC AR5
source: AR5 WG1
(IPCC 2013, Fig. SPM.1a)
source: AR5 WG1
(IPCC 2013, Fig. SPM.2) Arctic Sea Ice Annual mean Arctic summer sea ice start of satellite data Annual mean Arctic summer sea ice extent decreased very likely with a rate of 3.5-4.1% per decade in 1979 2012 decrease was most rapid in summer (high confidence). Data normalized to the satellite measured sea ice extent in 1979 source: AR5 WG1
IPCC AR5
IPCC AR5
Sea Level Variations Observed rate of global sea level rise and estimated contributions from different sources Rate of sea level rise (mm/year) Source of sea level rise 1961 2003 1993 2003 Thermal expansion 0.42 ± 0.12 1.6 ± 0.5 Glaciers and ice caps 0.50 ± 0.18 0.77 ± 0.22 Greenland Ice Sheet 0.05 ± 0.12 0.21 ± 0.07 Antarctic Ice Sheet 0.14 ± 0.41 0.21 ± 0.35 Sum of individual climate contributions to sea level rise 1.1 ± 0.5 2.8 ± 0.7 Observed total sea level rise 1.8 ± 0.5 3.1 ± 0.7 Difference (Observed minus sum of estimated climate contributions) 0.7 ± 0.7 0.3 ± 1.0 Note: Data prior to 1993 are from tide gauges and after 1993 are from satellite altimetry. Source: Bindoff et al., 2007
Sea Level IPCC AR5
Average exposure to cyclones in 1970 and 2030 (in thousands of people per year; assuming constant hazard) Source: IPCC SREX (2012)
Projections for Tropical cyclones SRES scenario A1B. Periods: 1961 90 (20 th cent.) and 2071 2100 (21 st cent.) Experiments from: Muller and Roeckner (2006) Track density Track density Mean intensity (10-5 s -1 ) White lines: p-values < 5% Difference in tropical easterly wave and cyclone statistics for 850 RV, between the 21C and 20C periods (21C 20C), averaged over the three ensemble members before differencing. Mean intensity differences are only plotted where the track density is greater than 0.5 per month per unit area. Source: Bengtsson et al. (2006) using TRACK
Important points on Climate Projections The global mean climate responses to the different scenarios start to diverge after about 2030. Before that, climate change is similar in all scenarios. However the full estimate of uncertainty from all sources still has a large degree of overlap in year 2100. For a given climate model, the temperature and precipitation changes in the different scenarios differ mainly in magnitude and timing. For a given scenario, there is significant variation in temperature and precipitation changes among the different climate model simulations due to differences in model formulation.
Observed variability in India s Monsoonal Climate Krishna Kumar, 2009
Goswami et al., 2006
All India Mean Annual Temperature Anomalies (1901-2007) (Base: 1961-1990) Krishna Kumar, 2009
Simulations over India for the 1901 2098 period Annual surface temperature over India Annual cycle of temperature and rainfall over India Monsoon rainfall over India The grey lines indicate the ensemble, the black line is the ensemble mean and the blue line is the observed. The red line is the ensemble member corresponding to the Hadley Center coupled model. Standard deviation (mm) and monsoon-enso correlation, for the observational (1901 2000) period Krishna Kumar et al., 2009
Projected changes in daily maximum temperature and daily rainfall Pre-monsoon (MAM) Tmax for the baseline period (1961 1990). projected future (2071 2100 minus 1961 1990 mean) change. Projected future Projected change change in number in the intensity of rainy days (mm/day) of (rainfall >2.5 mm) rainfall on a rainy during monsoon day. season (JJAS). Krishna Kumar et al., 2009
Possible impacts on India s Monsoonal climate Extremes in rainfall and temperature Onset and advance of Monsoon Active/break cycles Intensity and frequency of Monsoon Depressions
Modelling Products Extreme Scenario Moderate Scenario
Leiserowitz (2011)
Cook et al., (2013) examined ~12000 peer-reviewed papers in climate science literature and found that 97% of the papers said that global warming is happening and human-caused, at least in part.
Thank you saurabh.bhardwaj@teri.res.in