AOS102 Climate Change and Climate Modeling Post-midterm Review* *Yes, the final is cumulative---but weighted towards the second half Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

The transition into the 1998-98 La Niña cold phase (May 1998) Ch. 4, cont’d. El Niño and Year-to-Year Climate Prediction The transition into the 1998-98 La Niña cold phase (May 1998) 3Dnino-may98.jpg Figure 4.9a Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

An ensemble of forecasts during the onset of the 1998-99 La Niña ecmwf_forecast.1098.clr.eps Figure 4.18 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Jet stream and storm track changes associated with El Niño or La Niña Figure 4.21 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Probability distribution of precipitation and surface air temperature to El Niño and La Niña probability.dist.eps Figure 4.22 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Effect of ENSO on number of Atlantic “named storms” (tropical storms and hurricanes) in July-Oct. each year Figure 4.24 avg 8-9 Regression: La Niña ~10 El Niño ~6 But large scatter (& increases w earlier SST) Hurricane season often June-Nov, here avg through months with most storms Tropical storm: sustained winds > 18 m/s; hurricane: winds > 33m/s (74 mph); Category 5 > 69 m/s Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Climate Models Figure 5.1 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Topography of western North America at 0.3° and 3.0° resolutions 5.1.c Resolution and computational cost Topography of western North America at 0.3° and 3.0° resolutions US_.5deg.grid.eps, US_5deg.grid.eps Figure 5.3 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

5.1.c Resolution and computational cost Computational time = (computer time per operation) ´(operations per equation)´(No. equations per grid-box) ´(number of grid boxes)´(number of time steps per simulation) Increasing resolution: # grid boxes increases & time step decreases Half horizontal grid size Þ half time step (why? See below) Þ twice as many time steps to simulate same number of years Doubling resolution in x, y & z Þ 2´2´2´(# grid cells) ´2´(# of time steps) Þ cost increases by factor of 24 =16 In Fig. 5.3, 5 to 0.5 degrees Þ factor of 10 in each horizontal direction. So even if kept vertical grid same, 10´10´(# grid cells)´10´(# of t steps)= 103 Suppose also double vertical res. Þ 2000 times the computational time i.e. costs same to run low-res. model for 40 years as high res. for 1 week To model clouds, say 50m res. Þ 10000 times res. in horizontal, if same in vertical and time Þ 1016 times the computational time … and will still have to parameterize raindrop, ice crystal coalescence etc. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Vertical column showing parameterized physics so small scale processes within a single column in a GCM Figure 5.2 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Sea surface temperature climatology - January Observed SST (Reynolds data set, 1982-2000) Sea surface temperature climatology - January sst_clim_clr_wmap.eps Sea surface temperature climatology - July Revised Figure 2.16 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Sea surface temperature climatology - January NCAR_CCSM3 coupled simulation climatology (20th century run, 1979-2000) Sea surface temperature climatology - January Sea surface temperature climatology - July Figure 5.21 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

July precipitation climatology January precipitation climatology prec_clim_clr.eps July precipitation climatology mm/day Figure 2.13 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Observed (CMAP) and 5 coupled models 4 mm/day precip. contour December-February Coupled simulation precipitation climatology (20th century run, 1979-2000) 4mm_pr_djfjja_4ar_7900c70.eps June - August Figure 5.20 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Global Warming CO2 increases due to fossil fuel emissions. Figure 1.1 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Global mean surface temperatures estimated since preindustrial times Figure 1.3 Anomalies relative to 1961-1990 mean Annual average values of combined near-surface air temperature over continents and sea surface temperature over ocean. Curve: smoothing similar to a decadal running average. From University of East Anglia Climatic Research Unit, following Jones and Moberg (2003). Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Pathways of energy transfer in a global average global.energy.bal.clr.eps Figure 2.8 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

The Greenhouse Effect and Climate Feedbacks Surface temperature (C) as a function of absorptivity ea Increased absorption of infrared radiation by greenhouse gases leading to surface warming aD Ts = G + Water vapor feedback: warmer Þ more H2O vapor, GHG (see Fig 6.5) Figure 6.3 Figure 6.4 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Snow/ice feedback in the global energy balance Figure 6.7 Effects of cloud amount in the global energy balance Tend to cancel Small Figure 6.8 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

6.3b Climate sensitivity Mean, standard deviation, and range of doubled-CO2 climate sensitivity for a number of models Table 6.2 Publication Number of models Mean Standard deviation Range IPCC (1996) IPCC (2001) IPCC (2007) 17 15 18 3.8 3.5 3.2 0.8 C 0.9 C 0.7 C 1.9 to 5.2 C 2.0 to 5.1 C 2.1 to 4.4 C The mean, standard deviation, and range of doubled-CO2 climate sensitivity (global averaged surface temperature response) from models included in the two most recent IPCC reports. Range refers to highest and lowest values from among all models. Double CO2 & run the simulation to new equilibrium climate state. Change in the long term average defines doubled-CO2 response. Global-average surface temperature response DT2x used as a measure of climate sensitivity: doubled-CO2 climate sensitivity. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP A transient response experiment where greenhouse gas emissions are suddenly stopped at time ts, so the forcing stabilizes (upper panel) Idealized case: cap GHG at given level (i.e., stop emissions suddenly!) Temperature was less than equilibrium due to lag so continues to rise for several decades forcing_response3.eps Figure 6.12 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

IR to space due to Ts increase lag due to ocean, depends on  A transient response experiment by climate models of different climate sensitivities to forcing Heat storage balances GHG initially Initially small C + Ts = G ∂Ts ∂t Ocean heat storage IR to space due to Ts increase Radiative forcing (GHG) *to see this try Ts = in Eq. 6.15 using G = gt g(t - )  *= C Ts = G  in equilibrium Fig6.14_DT_t.eps Full solution DTs=(g/alpha)[t-tau(1-exp(-t/tau))]; Values used: alpha from Table 6.1 alpha = 0.9 to 1.6 cw=4200 J/(kgK), rho=1000 kg/m3, H=200 m. C =8.4e8 so tau = C/alpha gives for low alpha:   8.4e8/.9 =29.6; High alpha: 8.4e8/1.6=16.6 yr G reaches 4 W m-2 in 70 years so g= 0.057 W m^-2 yr^-1 roughly like 1%/yr CO2 incr. High sensitivity model (smaller ) lag due to ocean, depends on  Hard to distinguish high  from low initially Low sensitivity model Figure 6.14 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Global average warming simulations in 11 climate models Global avg. sfc. air temp. change (ann. means rel. to 1901-1960 base period) Est. observed greenhouse gas + aerosol forcing, followed by SRES A2 scenario (inset) in 21st century (includes both GHG and aerosol forcing) glbtmpanomtseries_ar4_c.eps Figure 7.4 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Observed global annual ocean heat content for 0 - 700m layer Ocean heat content anomaly rel . to 1961-90 (black curve) i.e. global upper ocean heat storage in response to accumulated heat flux imbalance (surface + exchange with lower layers) [Heat content anom. = (temperature anom x heat capacity x density), integrated surface to 700m depth over global ocean area] [For refc: 1 Wm-2 surface heat flux anom. = 1.1x1022 J/yr over 3.6x1014m2 ocean] Shaded area = 90% confidence interval Variations: natural variability and sampling error From glac.seaice.heatcont.sealev.eps (WG1AR4_heatcont.jpg). Pg. 390 Fig 5.1 Bindoff et al 2007 Removed curves for clarity: Red curve update rel. to 1961-90 (Ishii et al. 2006) Green curve update rel. to 1993-2003 Willis et al. (2004) (0 to 750m) Figure 7.18 After Bindoff et al (2007); data from Levitus et al. (2005) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Observed annual average anomalies of global mean sea level (mm) 1961 to 2003 trend in global mean sea level rise est. ~ 13 to 23 mm/decade Red reconstructed sea level fields rel. to 1961-90 [tide gauges avgd using spatial patterns from recent satellite data; Church & White, 2006] Blue curve coastal tide gauge measurements [rel. to 1961-90; alt method; Holgate & Woodworth, 2004] Black curve satellite altimetry rel. to 1993-2001 (After Bindoff et al 2007) Error bars denote 90% confidence interval From glac.seaice.heatcont.sealev.eps (WG1AR4_sealev.jpg). From AR4WG1_chap5 pg 510 Fig 5.13 Bindoff et al 2007 Refs: reconstructed sea level fields rel. to 1961-90 (updated from Church & White, 2006); coastal time gauge measurements rel. to 1961-90 (from Holgate & Woodworth, 2004); satellite altimetry rel. to 1993-2001 (Leuliette et al., 2004) ~ 13 to 23 mm/decade* [*> sum of est. trends from land ice and thermal expansion] Figure 7.19 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP 6.8b A doubled-CO2 equilibrium response experiment 6.8c The role of oceans in slowing warming Equilibrium temperature response Years 60-80 of time-dependent temperature response Annual average surface air temperature response from an earlier version of the GFDL climate model comparing equilibrium response to time-dependent response manabe_clr.eps Ratio of time-dependent response to equilibrium response Figure 6.13 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

7.1.c Commonly used scenarios Radiative forcing as a function of time for various climate forcing scenarios Top of the atmosphere radiative imbalance Þ warming due to the net effects of GHG and other forcings 4AR_scenario_forcings_c.jpg from the Special Report on Emissions Scenarios SRES: A1FI (fossil intensive), A1T (green technology), A1B (balance of these), A2, B2 (regional economics) B1 “greenest” IS92a scenario used in many studies before 2005 Figure 7.2 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Radiative forcing and global average surface temperature response 7.2 Global-average response to greenhouse warming scenarios Radiative forcing and global average surface temperature response Change in radiative forcing (Wm-2) From glb.rad.frc_Tchng.eps (glb.rad.frc_Tchng_a.jpg, glb.rad.frc_Tchng_b.jpg) Change in temperature (K) (after Mitchell & Johns 1997) Figure 7.3 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP 7.3 Spatial patterns of the response to time-dependent warming scenarios 2010-2039 Response to the SRES A2 scenario GHG and sulfate aerosol forcing in surface air temperature relative to the average during 1961-90 from the Hadley Centre climate model (HadCM3) HdCMGSa1_205080.clr.jpg 2040-2069 2070-2099 Figure 7.5 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP GFDL- CM2.0 30yr. avg annual surface air temperature response for 3 climate models centered on 2055 relative to the average during 1961-1990 NCAR- CCSM3 3modccsm3_4AR_tasa_ann50s_c.jpg MPI- ECHAM5 Figure 7.7 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Multi-model ensemble avg. January and July precipitation change for 10 model ensemble average for 2070-2099 minus 1961-90 avg (SRES A2 scenario) Figure 7.9 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

7.3.c Summary of spatial patterns of the response Poleward amplification of the warming is a robust feature. It is partly due to the snow/ice feedback and partly to effects involving the difference in lapse rate between high latitudes and the tropics. In time-dependent runs polar amplification is seen first in the northern hemisphere, while the North Atlantic and Southern Ocean effects of circulation to the deep ocean slow the warming. Continents generally tend to warm before the oceans. There is a seasonal dependence to the response. For instance, winter warming in high latitudes is greater than in summer. The models tend to agree on continental scale and larger, but there are many differences at the regional scale. Regional scale predictions (e.g. for California) tend to have higher levels of uncertainty, esp. for some aspects (e.g. precipitation) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

7.3.c Summary of spatial patterns of the response (cont.) Natural variability will tend to cause variations about the forced response, especially at the regional scale. Precipitation is increased (about 5%-15%) on a global average, but regional aspects can be quite variable between models. There is reason to believe that regional changes are likely. Wintertime precipitation tends to increase. Summer soil moisture tends to decrease. This is an example of an effect that would have implications for agriculture. But soil moisture models depend on such things as vegetation response, which are crudely modeled and have much regional dependence (hence higher uncertainty). Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP 7.4 Ice, sea level, extreme events 7.4.a Sea ice and snow Simulated ice fraction change (2070-99) minus (1961-90) as a percent of the base climatol. ice fraction Sep. - Nov. Dec. - Feb. ech5_pred_ice_sondjf2080_c.eps Echam5 SRESA2 Figure 7.10 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

7.4.b,c (Projected future) Land ice & Sea level rise Sea level rise due to thermal expansion in GCMs ~0.13 to 0.32 m in 21st Cent. (1980-99 to 2090-99; A1B , similar for A2) (~13±7 mm/decade to 2020) Deep ocean warming continues, e.g., 1-4 m rise if stabilize at 4xCO2 Warming impact on Greenland and Antarctic ice sheets poorly constrained Greenland eventual melting ~7m over millennial time scale Most of Antarctica cold enough to remain below freezing Ice sheet dynamics complicated: “calving” of icebergs, … flow rate; Surprises, e.g. Larsen B ice shelf; monitoring, …. 7.6.c. Observed Sea ice, land ice, ocean heat storage and sea level rise: Trends: decrease; decrease; increase; increase; ~Consistent with predicted. 1961 to 2003 trend in global mean sea level rise ~ 13 to 23 mm/decade Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

7.4.d Extreme events If standard deviation of daily temperatures remains similar as mean temperature rises Þ more frequent occurrence of events currently considered extreme e.g., heat waves Few events above 40C (104F) (shaded area) temp.probability.dist.eps Much more frequent (shaded area many times larger) Mean change Figure 7.13 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Summary of predicted climate change Temperature The lower atmosphere and Earth's surface warm (the stratosphere cools). The surface warming at high latitudes is greater than the global average in winter but smaller in summer. (In time dependent simulations with a full ocean, there is less warming over the high latitude southern ocean). surface warming smaller in the tropics, but can be large rel to natural variability For equilibrium response to doubled CO2, global average surface warming likely lies between +2C and +4.5C, with a most likely value of 3C, based on models and fits to past variations. "Best-estimate” (IPCC 2007) temperature increase in 2090-99 relative to 1980-99 depends on future emissions. For A2 scenario 3.4C; B1 1.8C; A1B 2.8C,;A1FI 4.0C. Likely ranges est at 60% to 160% of these values (actual model ensemble ranges are smaller) Due to the thermal inertia of the ocean, the temperature would increase for decades beyond whatever time stabilization of greenhouse gases might be achieved. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Summary of predicted climate change Precipitation The global average increases (as does average evaporation); the larger the warming, the larger the increase. Precipitation increases at high latitudes throughout the year; for equilibrium response to doubled CO2, the average increase is 3 to 15%. The zonal mean value increases in the tropics although there are areas of decrease. Shifts in the main tropical rain bands differ from model to model, so there is little consistency between models in simulated regional changes. Soil Moisture Increases in high latitudes in winter. Decreases over northern mid-latitude continents in summer (growing season). Snow and Sea-Ice The area of sea-ice and seasonal snow-cover diminish. Sea Level Sea level increases excluding rapid changes in ice flow for 2090-99 relative to 1980-99: for A2 0.23-0.51m, B1 0.18-0.38; even if greenhouse gases are stabilized deep ocean warming creates ongoing sea level rise for centuries. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Fig. 7.15 (will be expanded with supplementary figs. below) 7.5 Climate change observed to date Amplitude of natural variations depends on the spatial and time averages considered. much of weather/climate T variability due to heat transport anomalies; but these tend to cancel in large regional averages anthropogenic trend in temperature expected to have large spatial scales; i.e. clearer relative to noise in large-scale avgs CRUtasa_NShemUSEurGerDC.jpg Orig. files in AS102/as102_obsTa_timeser_lab Fig. 7.15 (will be expanded with supplementary figs. below) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Observed 20th C. temperature for various averaging regions with climate model simulated range: natural only vs. natural + anthropogenic forcings Observed warming exceeds range that can occur by natural variability in models AR4WG1_SPM-4.eps Hegerl et al chap 9, also SPM IPCC 2007 Figure 7.16 (after Hegerl et al. 2007) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

SRES Multi-model mean surface warming projections A2, A1B, B1 Multi-model mean surface warming projections as a continuation of 20th-century simulation Warming incr with forcing Potential warming > current Constant composition (2000 values) simulation, forcing kept at year 2000 level (gives global warming commitment) + Constant composition commitment simulations from A1B and B1 2100 values AR4WG1_sfcwmproj_SRESforce.eps fig TS-32, pg 79 of WG1 Technical Summary Solomon et al. 2007 Figure 7.20 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Annual multi-model mean surface air temperature change (relative to 1980-1999 clim.) A2: 2080-2099 7 7 6 6 4.5 5 5 4 3.5 5 4.5 4 4 4.5 4 3 3.5 3 AR4WG1_10.8_glb.projA2c.jpg. From AR4WG1_chap10 Pg. 766 Fig 10.8 Meehl et al 2007 4 2 4 4 3 3 Figure 7.21 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Annual multi-model mean surface air temperature change (relative to 1980-1999 clim.) B1: 2080-2099 4 5 4 3.5 3.5 3 3 2.5 2 2.5 2 2.5 2 2 AR4WG1_10.8_glb.projB1c.jpg. From AR4WG1_chap10 Pg. 766 Fig 10.8 Meehl et al 2007 2 2 2 Figure 7.21 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

7.8 The road ahead Mitigation scenarios estimating greenhouse gas emissions as a function of time (emissions pathways) that would lead to stabilization of greenhouse gases, i.e., eventually bring emissions to low levels so concentration stop increasing (Climate change mitigation: actions aimed at limiting the size of the climate change; Adaptation, actions that attempt to minimize the impact of the climate change) Mitigation scenarios shown as center of a range of emissions for six categories (CO2 emissions shown as a function of time; other greenhouse gases follow a similar paths). Range for each category shown as error bar in 2050 Figure 7.22 Values condensed from Barker et al., 2007 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Recall for long-lived gas, Values condensed from Barker et al., 2007 Categories IV-VI emissions continue to increase over the first decades ~ recent trends, modest societal action Recall for long-lived gas, Constant emissions Þ ongoing increase of concentration; Increasing emissions Þ concentration increases at ever faster rate; Decreasing emissions Þ concentration increases but less quickly Stabilization occurs for very low emissions. If emissions are not brought down quickly enough, CO2 overshoots stabilization target Þ negative emissions are required, i.e. methods for actively removing CO2 (categories I-II). Alternative: bring down emissions sooner. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

One way of visualizing contributions to the change in energy supply: a “wedge” in which a low emission technology grows from small contribution today to displace 1 PgC/yr of fossil fuel emissions 50 years from now (Pacala & Socolow, 2004) (25 PgC of emissions prevented overall) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Examples of scale-up required to give this (Pacala & Socolow, 2004) (each to displace 1 PgC/yr of fossil fuel emissions 50 years from now ) Doubling the fuel efficiency of cars Cutting in half the average mileage each car travels Energy-efficient buildings (reduce emissions by 25% including in developing world). Increase efficiency of coal-based electricity generation from 32% to 60% Wind power substituted for coal power (50 times current capacity). Photovoltaic power increased to about 700 times the current capacity to substitute for coal Nuclear power substituted for 700 GW of coal power (a doubling of current capacity). Biomass fuel production scaled to ~100 times current Brazil or US ethanol production Carbon capture and storage a factor of 100 times today’s injection rates or the equivalent of 3500 times the injection by Norway’s Sleipner project in the North Sea. Decrease tropical deforestation completely plus double current rate of tree plantation Conservation tillage applied to all cropland (10 times current). Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Roughly how many of these contributions are required to move from category VI emissions path to a lower emissions path? Category VI emissions increase by between 7 and 8 PgC/year over 1st 50 years Þ 7-8 of the above required just to keep emissions rates close to present values (in face of increasingly energy intensive economies and population growth) Category I requires emissions to decrease ~ 4 to 5 PgC/year in 50 years (~12 PgC/year relative to category VI) roughly 12 of the above items if started in 2000 (11 shown) Which approach? All of the above plus more. The 2C warming target is already challenging Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Understanding & predicting the climate system El Niño Global warming Climate models (complex & simple) + observations Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP