“Global  Land  Carbon  Cycle:  Data,  Theory,  and  Future  Projections”  	  Yiqi  LUO  Dept.  of  Microbiology  &  Plant  Biology  University  of  Oklahoma,  USA    Terrestrial	  ecosystems	  play	  a	  crucial	  role	  in	  the	  global	  carbon	  cycle	  and	  in	  the	  regulation	  of	  climate	   change.	   They	   absorbed	   nearly	   30%	   of	   anthropogenic	   CO2	  emissions	  in	   the	   past	   six	  decades.	  If	  that	  absorption	  capacity	  were	  to	  change,	  in	  either	  direction,	  it	  would	  have	  a	  large	  impact	  on	  atmospheric	  CO2	  concentrations,	  resulting	  in	  a	  strong	  feedback	  effect	  on	  climate.	  
To	  date,	  the	  magnitude	  of	  the	  terrestrial	  carbon	  sink	  has	  been	  deduced	  indirectly:	  combining	  analyses	   of	   atmospheric	   carbon	   dioxide	   concentrations	   with	   ocean	   observations	   to	   infer	   the	  net	   terrestrial	   carbon	   flux.	   In	   contrast,	   when	   knowledge	   about	   the	   terrestrial	   carbon	   cycle	   is	  integrated	   into	   different	   terrestrial	   carbon	   models	   they	   make	   widely	   different	   predictions	  and	  fit	  observations	  poorly.	  These	  problems	  have	  been	  known	  for	  more	  than	  a	  decade	  and	  obstruct	   our	   ability	   to	   adequately	   inform	   policy	   and	   decision	   makers	   about	   the	   probable	  consequences	  of	  anthropogenic	  emissions	  and	  land	  use	  change	  scenarios.	  
The	   modeling	   community	   has	   adopted	   a	   variety	   of	   different	   approaches	   to	   improve	   the	  terrestrial	   carbon	   models	   in	   ESMs,	   none	   of	   which,	   unfortunately,	   has	   led	   to	   significant	  reductions	   in	   the	   variation	   between	   model	   predictions.	   A	   common	   approach	   has	   been	   to	  incorporate	   an	   increasing	   number	   of	   processes	   known	   to	   influence	   the	   carbon	   cycle,	   to	  make	   the	   models	   as	   realistic	   as	   possible.	  However,	   the	   more	   processes	   the	   models	  incorporate,	   the	   more	   complex	   and	   less	   tractable	   the	   models	   are,	   making	   it	   practically	  impossible	   to	   understand	   why	   different	   models	   make	   different	   predictions.	   Model	  intercomparisons	   have	   been	   effective	   at	   revealing	   the	   extent	   of	   the	   differences	   between	  model	   predictions	   but	   have	   typically	   provided	   limited	   insights	   into	   its	   origins.	   Benchmark	  analyses	   have	   provided	   assessments	   of	   model	   performance	   against	   standard	   datasets,	   but	  so	  far	  been	  restricted	  to	  processes	  occurring	  over	  short	  time-­‐scales	  (e.g.	  days	  to	  years).	  Data	  assimilation	   methods	   have	   been	   applied	   to	   directly	   constrain	   simple	   models	   or	   model	  components	  with	  observations	  yet	  less	  extensively	  to	  global	  models.	  
Many	  research	  programs,	  involving	  observations	  and	  experiments,	  are	  underway	  to	  improve	  understanding	   of	   the	   terrestrial	   carbon	   cycle.	   Observations	   to	   characterize	   carbon	   cycle	  components	   over	   all	   continents	   on	   Earth	   are	   usually	   carried	   out	   by	   satellites	   or	   research	  networks.	   These	   have	   generated	   various	   regional	   and	   global	   data	   products,	   such	   as	   global	  maps	   of	   gross	   and	   net	   primary	   production	   (GPP	   and	   NPP),	   and	   regional	   and	   global	  distributions	   of	   soil	   carbon	   content	   and	   soil	   respiration.	   These	   data	   products	   have	   been	  extremely	   useful	   for	   improving	   of	   our	   understanding	   of	   the	   processes	   and	   properties	  underpinning	  patterns	  in	  terrestrial	  carbon	  cycle	  components.	  	  Experimental	  studies	  are	  also	  implemented	  to	  manipulate	  factors	  that	  are	  expected	  to	  vary	  as	  a	  consequence	  of	  climate	  change,	  such	  as	  elevating	  CO2	  concentrations,	  increasing	  ambient	  temperature,	  and	  altering	  precipitation	   rates.	   This	   enables	   direct	   insights	   into	   how	   ecosystems	   respond	   to	   such	  perturbations	   and	   have	   revealed	   some	   important	   new	   mechanisms,	   such	   as	   acclimation	   and	  adaptation	   of	   the	   carbon	   cycle	   to	   climate	   change.	   Nevertheless,	   they	   have	   yet	   to	   lead	   to	  better-­‐constrained	  predictions	  of	  the	  terrestrial	  carbon	  cycle.	  
In	   this	   talk,	   I	   will	   carefully	   use	   available	   data	   to	   examine	   the	   internal	   properties	   of	   the	  terrestrial	   carbon	   cycle.	   Those	   properties	   largely	   determine	   and	   constrain	   its	   dynamics	  everywhere	  on	  the	  Earth,	  and	  thus	  form	  the	  basis	  upon	  which	  general	  theory	  can	  be	  formed	  for	   the	   terrestrial	   carbon	   cycle.	   I	   then	   discuss	   how	   five	   classes	   of	   external	   forcing	   each	  influence	   the	   predictability	   of	   the	   terrestrial	   carbon	   cycle.	   Together,	   these	   classes	  encompass	   almost	   all	   possible	   scenarios	   that	   terrestrial	   ecosystems	   experience.	   Finally,	   I	  highlight	   key	   areas	   for	   empirical	   research	   to	   improve	   predictive	   understanding	   and	   outline	  strategies	  to	  realize	  the	  predictability	  in	  terrestrial	  carbon	  models.