The Instrumental Record

Question 1

the instrumental record

Answer 1

refers to pieces of evidence which can be collated to highlight global climate change

Question 2

interpolation

Answer 2

process in which statistical methods would be used to fill in gaps within the climate record -> uncertainty (Gulev et al., 2021)

Question 3

CO2 concentrations -> Mauna Loa Observatory (1960->)

Answer 3

increasing overtime -> peak during the 1997/98 El Niño (6th IPCC, Gulev et al., 2021).

Question 4

Temperature Records -> often divided based on land and SSTs and NH/SH

Answer 4

450 million reports across regions/times -> International Surface Temperature Initiative = 35,000 stations created for land temperatures (6th IPCC, Gulev et al., 2021)

Question 5

Temperature Record -> HadCRUT4 (1850->) -> collates HADGT3 and CRUTEM4 = 5x5 resolution (Morice et al., 2012)

Answer 5

84% of the data is taken from the surface -> Africa and poles = missing data (Cowtan and Way, 2014)

Question 6

Gridding -> anomalies at each latitude and longitude are collected -> step-change is analysed in comparison to local stations to determine whether it was anomalous or a climate signal -> produces 12 maps (1/month) and collates them into data points on a time series/year -> uses these to create grid boxes

Answer 6

anomalies -> needed to avoid biases and cover a greater spatial scale -> technicality would appear that the poles are cooling if absolute data taken (Rhein et al., 2013 - 5th IPCC Report)

Question 7

realisations -> model runs of the climatic conditions to determine the certainty of the data set

Answer 7

statistical means are then taken to represent the magnitude of warming in the grid boxes (Morice et al., 2012)

Question 8

HadCRUT4 -> does not use interpolation -> leaves regions without data as the average - problematic due to arctic amplification not being represented (were improvements in data sets from HadCRUT3-4 though with Siberia included)

Answer 8

Cowtan and Way (2014) -> filled in the gaps via interpolation/satellite data -> earth has warmed 2x faster in the last 15yrs than the data implies

Question 9

HadCRUT5 (Met Office) -> 5,000 stations

Answer 9

MSLOT (NOAA) -> 7,000 stations (Pidcock, 2015) -> digitisation has also improves spatial coverage (Morice et al., 2012)

Question 10

Land/Sea Data Sets -> GISS/GISTEMP (NASA) -> operates at 2x2 resolution and has 99% data so requires less interpolation (Pidcock, 2015; Cowtan and Way, 2014)

Answer 10

MSLOT (NOAA) and Japan Meterological Agency -> different resolution of grid boxes = similar trends though (Pidcock, 2015)

Question 11

World Meteorological Organisation and Global Climate Observation System

Answer 11

land stations -> smooth the data and share it through the WMO (Morice et al., 2021)

Question 12

5th IPCC Report -> 0.85 increase in ocean temp and land surface (1880-2021) -> small differences between data sets = reproducibility

Answer 12

clear obvious trend of increasing
1960-1970 -> aerosol emissions led to a plateau

Question 13

SST interannual/interdecadal variability

Answer 13

ENSO -> 2022 temp drop due to a La Niña -> 1997/98 temp rise due to El Niño (Gulev et al., 2021 - 6th IPCC Report)

Question 14

Arctic Amplification

Answer 14

poles are warming 2x quicker -> ice-albedo feedback -> melting ice has an albedo of 0.8-0.9 while polar water has one of 0.1 (Simmonds, 2015)

Question 15

SST records -> Southern Ocean and Atlantic Ocean

Answer 15

massive heat store -> facilitated by AMOC heat transfers (Cheng et al., 2017)

Question 16

SSTs -> 1,200 drifting buoys and 4,000 ships

Answer 16

1.5million observations monthly

Question 17

Corrections -> inhomogeneities and urbanisation account for <0.05 (Folland and Parker, 1995)

Answer 17

wooden buckets -> 0.42 correction as water evaporated from buckets and leaked out (Hartmann et al., 2013) -> engine room warmed the buckets but HadCRUT4 used an error model to amend this (Morice et al., 2021)

Question 18

Corrections -> instrumental data

Answer 18

historical SST -> 0.1 estimated error in dating at the start of the dating -> exacerbated by differences in ground/air observations

Question 19

Lower troposphere = warming

Answer 19

greenhouse effect enhanced so less heat released to stratosphere
tropospheric ozone increased 30-70% (1970-2010)
tropopause increased in height from 1980 (Gulev et al., 2021)

Question 20

Asymmetry in tropical upper atmosphere -> warmed 1k (2010-2020)

Answer 20

more SH warming than NH -> due to land asymmetry and differences in ozone concentrations (Ladstädter et al., 2023).

Question 21

stratosphere = cooling

Answer 21

decrease in ozone = less UV
Montreal Protocol 2013 -> Antarctic ozone hole so called to reduce HCFC production -> more recovery in the NH (Gulev et al., 2021)

Question 22

radiosondes -> measure atmospheric depth at certain temperatures (Hartmann et al., 2013)

Answer 22

ozonesondes -> measure ozone (Gulev et al., 2021)

Question 23

Global Navigation Satellite System -> measures the earth’s vertical profile

Answer 23

uses satellites and produces data to a high-resolution (Ladstädter et al., 2023)

Question 24

atmospheric interannual variability

Answer 24

Quasi-Biennial Oscillation (Ladstädter et al., 2023)

Question 25

Corrections - instrumental differernces e.g. satellites vs weather balloons (would underestimate temp change and would pop) (Ladstädter et al., 2023).

Answer 25

there was a slowdown in rates of warming in the troposphere -> CMIP5 models were unable to simulate this due to residual error/uncertainties -> highlights complexities (Santer et al., 2017)

Question 26

sub-surface ocean temps recording started in the 1960s

Answer 26

argofloats used -> 4,000 in 2018 now 3,887 in 2022 (Cheng et al., 2017)

Question 27

sub-surface ocean temp gaps -> if earth is split into 1x1 -> in 1960 <10% coverage, 2003 > 20% and 2015 <30% (Chen et al., 2016)

Answer 27

warming has occurred for upper-700m from 1871 and occured for 700m-2000m from 1971 (Gulev et al., 2021)

Question 28

PERSIANN-CDR (NCEI) -> 1982 to present day using IR and Microwave radiation on a 2.5x2.5 resolution -> not sufficient to show regional change

Answer 28

Rain Sphere -> finer resolution and more local observations using IR on geostationary satellites (Nguyen et al., 2018)

Question 29

Global Rain Gauge -> shares data

Answer 29

but limited global coverage - only sports field worth of area is monitored for precipitation (Kidd, 2017)

Question 30

Clausius Claperyon -> air can hold more water vapour as it warms (7% increase in temp) = more precipitation (Gulev et al., 2021)

Answer 30

trend complicated however -> often easier to split places into regions to examine -> consider variability e.g. NAO, ENSO = noise

Question 31

Precipitation increase = N.A, Tropical Africa, C.A, Maritime Continent, part of Eur

Answer 31

precipitation decrease = S.A., west N.A. north Africa, Middle East (Gulev et al., 2021)

Question 32

5th IPCC Report -> precipitation over the midlatitudes =

Answer 32

increase from 1901 (Hartmann et al., 2013)

Question 33

corrections precipitation -> big uncertainty between model projections = different variables and parameterisation (simplifies processes)

Answer 33

(Hartmann et al., 2013)

Question 34

Satellites used to determine precipitation rates -> based on idea that clouds cause preciptation

Answer 34

midlatitudes examines for eddies, tropics examined for deep, convective clouds -> satellites use IR to look at cloud top temperature to inform precipitation rates – but it is harder to get satellite data for the midlatitudes than the tropics as there is deeper convection in the tropics -> can also underestimate precipitation as warmer rain occurs from mountain induced uplift but would not be detected by cloud top temperature.

Question 35

Sea Level Rise = satellite monitoring via LIDAR altimeters (time it takes for the satellite to reach the surface and back)

Answer 35

e.g. Jason-2 and Poseidon/TOPEX (Nerem et al., 2018)

Question 36

3mm/year rise in global sea levels from 1933 = contribution of melting glaciers not thermal expansion (Nerem et al., 2018) -> global uniformity.

Answer 36

The 5th IPCC report -> sea levels increased 0.19m from 1901-2010, 1.7mm 1991-1993 and 3.2mm 1993-2010 (Rhein et al., 2013).

Question 37

SLR -> collected from the 1700s

Answer 37

1800s = tide gauges
1900s = satellites (Rhein et al., 2013)

Question 38

SLR variability

Answer 38

episodic variation -> Mt Pinatubo 1991 -> drop in sea levels and led to the TOPEX satellite being launched and impacted the start of the data set (Nerem et al., 2018)

Question 39

SLR correction

Answer 39

tide gauges -> implicated by variability as they are local -> while SLR signal during an ENSO needs eof analysis to make it more accurate (Nerem et al., 2018)

Question 40

problems with data = noise makes depicting the signal more difficult = corrections are required

Answer 40

calibrations, methods shifting, inhomogeneities etc… complicates the noise to signal ratio

Question 41

GRACE mission -> LIDAR/RADAR = ice thickness (Nerem et al., 2018)

Answer 41

Operation IceBridge -> more sophisticated tech to analyse ice sheet thickness -> lidar altimeter, snow camera etc (Lindsay and Schweiger, 2015).

Question 42

ice extent -> submarines and sonar or electromagnetic sensors on helicopters

Answer 42

more limitations due to seasonal variability and limitations in recording data (Lindsay and Schweiger, 2015).

Question 43

Sea ice extent NH = clear cumulative loss in ice, SH = trend harder to discern

Answer 43

West Antarctica e.g Thwaites Glacier = thinning, East Antarctica = thickening event despite CMIP modelling (Simmonds, 2015)

Question 44

AR5 highlighted that the Arctic loss around 1.3 to 2.3m of thickness between 1980 and 2008 indicating clear thinning (Gulav et al., 2021).

Answer 44

Antarctic sea ice change has been different, there has been a decrease in the volume of sea ice though this decrease was only by a small amount, with a loss of 1.2-1.8% from 1979 to 2012 -> entire regional change is small for Antarctica due to some regions thinning and others thickening (Gulav et al., 2021).

Question 45

glacial extent -> global decrease in mass from the 1970s and most in retreat (Gulev et al., 2021)

Answer 45

rates can be variable -> variability e.g. Hindu Kush-Karokoram-Himalaya region with low thinning around Karakoram -> due to the Asian Monsoon and westerly atmospheric circulation pattern (Kaab et al., 2021)

Question 46

ice extent corrections

Answer 46

need to account for the different techniques being drawn upon e.g. snow influences ULS (upward looking sonar) instruments but not Air-EM (plane-based electromagnetic waves) (Lindsay and Schweiger, 2015).